Naval ISR Fusion Principles, Operations, and Technologies 1630818941, 9781630818944

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Naval ISR Fusion Principles, Operations, and Technologies

For a complete listing of titles in the Artech House Intelligence and Information Operations Library, turn to the back of this book.

Naval ISR Fusion Principles, Operations, and Technologies Jim Scrofani Will Williamson Jihane Mimih Ed Waltz

Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the U.S. Library of Congress. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Cover design by Andy Meaden Creative

ISBN 13: 978-1-63081-894-4

© 2023 ARTECH HOUSE 685 Canton Street Norwood, MA 02062

All rights reserved. Printed and bound in the United States of America. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher.   All terms mentioned in this book that are known to be trademarks or service marks have been appropriately capitalized. Artech House cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark.   The views expressed in this publication are our own and do not imply endorsement by the Office of the Director of National Intelligence, the Department of Defense, or any other U.S. government agency.

10 9 8 7 6 5 4 3 2 1

We dedicate this book to the men and women of the United States Navy; we admire their foresight and leadership in developing, testing, evaluating, and deploying new technologies to expand the horizons of intelligence, surveillance, and reconnaissance. We wish them fair winds and following seas

Contents

Foreword

xi



Preface

xv

1

The Naval Intelligence Reconnaissance and Surveillance Mission

1

1.1

The Domain of Naval Operations

2

1.2

Naval Mission Concepts and ISR Roles

4

1.3

Principles of Distribution, Automation, and Speed

9

1.4

MDA and ISR Operations in Conflict and Warfare

12

1.5

About This Book

17

Endnotes

20

2

Principles of Operations from Seabed to Space

23

2.1

Maritime Awareness

25

2.2

Naval Subsurface and Seabed Domains

31

2.3

Naval Surface and Airborne ISR

34

2.4

Naval Space

35 vii

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Naval ISR Fusion Principles, Operations, and Technologies

2.5

Naval Cyber

40

2.6

A Seabed-to-Space Scenario

42

Endnotes

51

3

Distributed Maritime Operations

55

3.1

Introduction to DMO

56

3.2

DMO Tactical Considerations

58

3.3

DMO Architecture and Elements

60

3.4

All-Domain C2 Battle Management

62

3.5

A DMO Scenario

64

Endnotes

67

4

Naval Information Fusion Systems

69

4.1

Enterprise-Level Fusion

70

4.2

Information Systems Fusion

75

4.3

Naval C4ISR Challenges

82

Endnotes

84

5

All-Domain Fusion and Operation Challenges

87

5.1

Challenge 1: Spatial Distribution, Association, and Latency

90

5.2

Challenge 2: Temporal Sample Rate and Dynamic Targets 93

5.3

Challenge 3: Accuracy for Fire Control and Missile Engagement

5.4

Challenge 4: Integrating Cyber Capabilities Endnotes

98 103 105

6

Maritime MultiINT Fusion Processes

107

6.1

The JDL Model for Organizing Naval ISR Fusion

109



Contents

ix

6.2

Maritime Object and Situation Assessment Levels 1 and 2

114

6.3

Maritime Impact or Threat Assessment Level 3

120

6.4

Maritime Distributed Resource Allocation and Orchestration

122

6.5

Conclusion

126

Endnotes

127

7

Sensor Distribution and Adaptation

131

7.1

Sensor Networks and Grids

133

7.2

Advanced Wireless Networks

137

7.3

Functional Nodes on the Maritime Network

139

7.4

Distributed Data Fusion in Network Operations

144

Endnotes

152

8

The Role of AI, Automation, and Autonomy

155

8.1

Automating Naval Systems

157

8.2

Sensemaking in a Naval ISR Context

161

8.3

C2 Automation

167

8.4

Automation in the All-Domain ISR Fusion Scenario

172

Endnotes

172

9

Distributed Space Maritime Surveillance

177

9.1 9.1.1 9.1.2 9.1.3

Smallsat Constellations for Sensing and Communication 179 Visible Imagery: Planet Labs Flock 180 SAR: Capella 181 RF Signals Collection: HawkEye 360 182

9.2

A Networked Constellation for Ocean Surveillance and ISR

183

9.3

Contributions to Naval ISR

185

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Naval ISR Fusion Principles, Operations, and Technologies

9.4

Surveillance Constellation Performance with Examples 187

9.5 9.5.1 9.5.2 9.5.3

Performance Study Results Planet FLOCK–Based Solutions Capella-Based SAR Solutions Hybrid MultiINT Design

191 191 192 192

9.6

Conclusions

193

Endnotes

195

10

Future Technologies to Enable All-Domain

199

10.1

Naval Technology Scanning to Avoid Surprise

201

10.2

Undersea and Seabed Surveillance

205

10.3 10.3.1 10.3.2 10.3.3

Quantum Technologies in Naval ISR Quantum Computing Quantum Sensing Quantum Communication Endnotes

213 214 216 219 221



About the Authors

225



Index

229

Foreword As professional naval intelligence officers with keen interests in emergent dynamic intelligence, surveillance, and reconnaissance (ISR) processes, we needed to develop a significant appreciation for the challenge of integrating information from many sources, and the potential role of technology to contribute automation to achieve speed and accuracy. Working with a team of engineers in the 1980s, what came to be known as the Joint Directors of DoD Labs (JDL) Data Fusion model was developed—a reference architecture that remains today as a standard for integrating information in intelligence applications. Throughout the 1980s and 1990s, the model guided the development of many naval data fusion capabilities as new sensor and digital links enabled even more information to be fused to provide situation awareness to combat information centers across operating forces. Our requirement in those days was to integrate and derive threat information from the seabed to space. By the early 2000s, it was known that the U.S. Navy had a sound vision in Network Centric Operations (NCO) and an implementation concept in FORCEnet, but to achieve enterprise transformation, we needed to solve many challenges. The first was acquisition reform and transformation to enable rapid change at large scale—a continuing problem and issue in the DoD to this day. We needed a level of system-of-system (SoS) enterprise level systems engineering that was significantly more expansive than prior DoD projects—the closest was the air traffic network developed by the Federal Aviation Administration (FAA). We needed the highest level of continuous industry-government collaboration at both the enterprise and system/program level to accomplish networking and integration at the naval force scale. So, we had to apply software

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technologies to build composable enterprise services that worked at a large scale and across the naval networked hubs across the fleet, and many ashore facilities. Throughout the early years we also spent a lot of time thinking about what the naval officer needs to do and how it is done with automation—the role today we call human-machine teaming. This is an area that remains a challenge, how do humans and machines now coordinate all-domain unmanned and manned autonomous systems and sensors in what was to ultimately become even more challenging weaponized ISR? We recognized early on that the fusion process needed to be more distributed and adaptive—able to work across the vast network of navy sensors and sources, serving more distributed command and control (C2) capabilities. We believed that such a system would provide vastly improved situation awareness but also needed a means to characterize what and how much we don’t know— and figure out where we should be searching. The data fusion community was given many challenges. Now, two decades after the FORCEnet vison we have made significant technical and operational progress. Modern navies face challenges by adversaries that seek to control areas of the seas and littorals. Distributed sensors, ubiquitous cyber, and precise, hypersonic-fast long-range weapons enable large-area denial capabilities. To counter these threats, distributed maritime operations (DMO) have become more the order of the day that relies even more critically on secure and assured networks for ISR and BMC2. Naval strategies, operations, and tactics are supported by complex technologies that are intended to bring networked centric warfare to the forefront—and continuously transforming naval ISR. This book is about that transformation. A team of professors at the Naval Postgraduate School that understand this area well have compiled this comprehensive book to organize and explain the advanced methods and capabilities in naval ISR, and the impact on naval operations. The authors are, appropriately, from the school’s Center for Multiple Intelligence (MultiINT) interdisciplinary studies, and they bring a breadth of knowledge about the various INTs and the processes to bring them together into a coherent picture of the naval battlespace. In this book, the authors move from the need for distributed maritime operations, to the enabling ISR technologies—from satellite constellations to integrated unmanned systems and quantum sensing. An important feature of this book is the breadth and depth of scholarly literature reviewed to allow the reader to go beyond the pages of the book and explore the indispensable technical literature in the field of data fusion. The authors explain the fundamentals of ISR, the networking of sensors from seabed to space, and the methods of data fusion—corelating and combining data from the vast collection network into a correlating view.



Foreword

xiii

This is an important book for the naval officer, developer, or system implementer in advanced ISR, and for those seeking a deeper understanding of the challenges, operations, and methods of advanced naval ISR systems. Admiral William O. Studeman, USN, Ret. March 2023

Preface It is a time of rapid change in maritime strategy, naval operations, and fleet tactics. Technology is a key driver for this change as intelligence, surveillance, reconnaissance, and targeting (ISRT) is enabling faster, more precise, and longer-range maritime operations. Maritime information superiority is the goal sought by modern navies to enable this new speed, precision, and reach for the ocean-going fleet. In this book we introduce the elements, both ISRT processes and technologies, that are enabling increasing degrees of maritime superiority and the operations that are enabled by it. We provide comprehensive citations to the open literature and relevant books to guide the readers who desire to dive deeper into the wide range of technical subjects that we cover. We necessarily emphasize the advanced technologies that are enabling new fleet operations and tactics that include more widely distributed vessels, unmanned vehicles and vessels, and the distribution of sensors from the seabed to space. While we describe current ISR operations and technologies, we also emphasize future capabilities that will enable the distributed fleet; many of these capabilities are not operational at the time of this writing, but we anticipate will soon join international navies. Among the advanced topics we cover is the operational concept of DMO and how advanced ISRT capabilities enable DMO and its distributed lethality. The U.S. Navy has adopted the term seabed to space to encompass the notion of sensors and operations across the full range of domains in which the Navy operates—this book also addresses that breadth of distribution, as well as the concept of network centric operations.

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As a team of coauthors, we are also colleagues in the Center for MultiINT Studies (CMIS), an interdisciplinary research center in the Electrical and Computer Engineering (ECE) department at the Naval Postgraduate School (NPS), Monterey, California. We conduct cutting-edge research and produce research outcomes to advance the capabilities to integrate data and information from multiple intelligence domains (INTs). This book introduces what we do—from the seabed to space. We express our gratitude for the support in this effort from Dr. Douglas Fouts, chair of the electrical and computer engineering at NPS, our colleagues at NPS, and to our research sponsors that have always encouraged our efforts in advancing the state of the art in ISR. We thank Mr. Mark Owen of the Naval Information Warfare Center–Pacific for his encouragement and guidance in our research, as well as the encouragement of numerous DoD space experts that we cannot name here. We are also grateful for the excellent support from our Artech House editors, and the excellent technical reviewer that significantly enhanced the manuscript with a comprehensive technical review. We are honored that Admiral William Studeman, the recognized Navy pioneer in data fusion, has graciously written the foreword to this book.

1 The Naval Intelligence Reconnaissance and Surveillance Mission The navies of the world play a unique role in national security, providing border security, assured maritime access, and forward deployed deterrence. Navies are employed to project influence and power, and the maritime domain is viewed as a maneuver space for that projection. Navies are unique in that they move and maneuver forces within international waters without prior diplomatic agreement, and can be self-deploying, self-sustaining, sea-based, and operate as an expeditionary force. The goal of these forces is to achieve maritime superiority—that degree of dominance of one force over another that permits the conduct of maritime operations by the former and its related land, maritime, and air forces at a given time and place without prohibitive interference by the opposing force Department of Defense (DoD). A key element of all naval operations is the understanding of the maritime environment, provided by operations and activities of intelligence, surveillance, and reconnaissance (ISR), highlighted in this book: Intelligence, surveillance, and reconnaissance (ISR)—1. An integrated operation and intelligence activity that synchronizes and integrates the planning and operation of sensors, assets, and processing, exploitation, and dissemination systems in direct support of current and future operations. 2. The organizations or assets conducting such activities (DoD JP 2-01) [1].

The U.S. Navy also emphasizes the close relation of ISR to command and control (C2), and computers (C4) that direct operations by applying the 1

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Naval ISR Fusion Principles, Operations, and Technologies

term C4ISR to complete the relationship between the information collection understanding role (intelligence) and the warfighting response role applying command, control, communications, and computation (C4). This chapter introduces the naval mission and the drivers that are placing demands on new modes of operations and the advanced ISR and data fusion capabilities that are required to support them, especially in the attributes of ISR distribution, automation, and speed.

1.1  The Domain of Naval Operations C4ISR is conducted across the entire naval areas of operations defined by terminology that distinguishes the unique areas that make up the maritime domain, comprised of oceans, seas, bays, estuaries, islands, coastal areas, and the airspace above these, including the littorals (Table 1.1). Navies uniquely operate at the boundary of land, sea, and air and now include the boundaries to space above and cyberspace. The physical attributes of each of these areas constrain and influence naval operations and the ISR process. The domain of maritime operations is also defined in international law by the United Nations Convention on the Law of the Sea (Figure 1.1) to distinguish nationally sovereign areas of the airspace and territorial sea (12 miles from baseline coastal boundary) out to the economic (activity) exclusion zone out to 200 miles from the coast baseline. Navies face a difficult maritime target challenge to maintain continual custody of maritime threats (subsurface to space adversary weapon systems and naval sensing systems that provide surveillance-to-targeting custody information for commanders). This can only be achieved by dynamically orchestrating

Table 1.1 Layers and Waters of the Maritime Domain The Maritime Domain Cyberspace Domain Within the Global Information Environment Space Communication, Navigation, and Intelligence Collection The Airspace Above Brown The Littorals Green Blue Water Water Water Landward Seaward Navigable Area inland from Area from the Area of coastal The open ocean rivers and their the shore that can open ocean to the waters, ports, and and the deep estuaries be supported and shore that must harbors seabed below defended directly be controlled from the sea to support operations ashore The shallow water floor, waves, currents, vegetation The seabed below



The Naval Intelligence Reconnaissance and Surveillance Mission

3

Figure 1.1  Maritime zones recognized by international law [2].

the Navy seabed-to-space sensor network to assign sensors to targets optimally and dynamically. In later chapters we describe this process, referred to as level 4 fusion, in detail. These ISR capabilities are required to have the attributes of speed, flexibility, agility, and scalability to address the increasing scope of the maritime environment. New ISR technology is enabling ubiquitous sensing of the world’s oceans from all of the domains that go from seabed to space. Platform technologies coupled with artificial intelligence (AI) to increase autonomy and speed of operation are enabling new classes of sensing systems that will significantly expose the oceans. Consider the six key domains of this exposure: • Seabed: The deep ocean (6,000–36,000 ft) is the newest potential domain of warfare where undersea cables, undersea infrastructure (oil and gas), mines, and future deeply hidden weapon systems reside and provide targets in conflict. This is the realm of deep submersible remotely operated and autonomous systems. • Undersea: Traditional undersea warfare depths to 6,000 ft are the operating areas of manned and unmanned submersible weapons and sensing systems; this is the realm of submarine and antisubmarine warfare (ASW). • Surface: Surface ships and unattended sensors (buoys, autonomous station–keeping sensor nets, etc.) provide surface level, overt, and covert maritime sensing. • Information and cyber: This nonphysical domain has a presence across all of the physical domains. The U.S. DoD defines the information environment (IE) as the aggregate of individuals, organizations, and systems that collect, process, disseminate, or act on information, a heterogeneous

4

Naval ISR Fusion Principles, Operations, and Technologies

global environment where humans and automated systems observe, orient, decide, and act on data, information, and knowledge. DoD then defines cyberspace as a global domain within the IE consisting of the interdependent network of information technology, infrastructures, and resident data, including the internet, telecommunications networks, computer systems, and embedded processors and controllers. Cyberspace operations targeting the physical and informational dimensions of the IE also have an impact in the human cognitive dimension [3]. • Air: Manned and unmanned airborne platforms are tasked to cover key areas of naval operafor patrol observation and influence. • Space: Individual spacecraft, constellations, and clusters of networked spacecraft provide persistent coverage of the world’s oceans (primarily radar and electro-optical imaging, and signals collection).

1.2  Naval Mission Concepts and ISR Roles Navies uniquely operate on the world’s seas and enable operations beyond a nation’s sovereign territory, extending defense and projecting a nation’s power to defend and influence in the nation’s vital interests. This also engages the naval force with foreign nations in cooperation and partnerships at sea (e.g., protecting seaways, interdicting illicit operations), and ashore (defense partnerships, training, etc.). Navies must be prepared to operate day-to-day and escalate/de-escalate across the spectrum from competition to lethal combat (Figure 1.2). A number of key operational concepts and the terms associated with them must be understood to appreciate the drivers and demands that are being placed on future naval ISR systems: • Maritime superiority is that degree of dominance of one force over another that permits the conduct of maritime operations by the former

Figure 1.2  The competition-to-conflict spectrum [4].



The Naval Intelligence Reconnaissance and Surveillance Mission

5

and its related land, sea, and air forces at a given time and place without prohibitive interference by the opposing force. • Maritime domain awareness is the effective understanding of anything associated with the maritime domain that could impact the security, safety, economy, or environment [5]. • Information dominance is the operational advantage gained from fully integrating the Navy’s information functions, capabilities, and resources to optimize decision-making and maximize warfighting effects. It provides the ability to seize and control the information domain high ground when, where, and however required for decisive competitive advantage across the range of Navy missions [6]. • Sea control is the employment of naval forces, supported by land and air forces as appropriate, in order to achieve military objectives in vital sea areas. Such operations include destruction of enemy naval forces, suppression of enemy sea commerce, protection of vital sea lanes, and establishment of local military superiority in areas of naval operations. See also land control operations (U.S. DoD) [7]. • Distributed maritime operations (DMO) is an operational concept that enables widely dispersed naval units to perform sensing, command, and control, and weapon activities such that the distributed platforms act as a coherent whole. This is related to: • Distributed lethality is an operational concept to more widely distribute lethal weapon systems across surface ships (“if it floats, it fights”) allowing all surface units to contribute to coordinated and independent surface strike actions. • Dynamic force employment (DFE) is an operational concept that deploys naval forces over wider and more diverse sets of environments, rather than establishing regular maritime patterns of operation. • All-domain ISR is an ISR that encompasses and integrates information from all domains of the maritime environment; sensors and sources from seabed to space to provide commanders with the most complete picture of adversary activities. In addition to these operational concepts, maritime operations are confronted by significant threat challenges that are the basis for these new concepts of operation: • Great power competition is a geopolitical theory that characterizes the world as a competition that is taking place not only in political, military,

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Naval ISR Fusion Principles, Operations, and Technologies

economic, social, and information domains, but also in the domain of ideas (or ideologies) about the state of the world, the goals for the world, and what should be done to achieve those goals. It is a competition for influence, and therefore power [8]. • Gray zone competitive strategies is the gray zone between peace and combat and includes competitive strategies that seek to gradually alter the status quo of power by employing unconventional elements of state power to influence and assert control. Gray zone operations are not conducted directly by the military, but by proxies (unidentified groups, disguised military, law enforcement, civilian organizations, sympathizers) using the pretexts (e.g., righting wrongs, protecting certain innocents). Academics have specifically identified the adoption of the gray zone operational approaches in great power competitions to mitigate threats to contested maritime areas. Russia and China are increasingly turning to these tactics to pursue objectives on their periphery without reaching the level of violence that could provoke a United States or allied response. In gray zone operations, an aggressor denies its adversary physical access to contested areas at sea or ashore using civilian or paramilitary forces, spreads disinformation via social media to foment protests and insurgencies, and degrades the defender’s computer networks and sensors through cyber and electronic warfare attacks. For China, these gray zone tactics represent an attempt to pursue its interests in the near-term without drawing a full-scale response by the United States, its allies, and others. In contrast, gray zone approaches are a long-standing component of Soviet and Russian military and intelligence operations [9]. • Antiaccess/area-denial (A2/AD) is an operating environment protected by: (1) antiaccess enemy actions that inhibit military movement into a theater of operations, and (2) area-denial activities that seek to deny freedom of action within areas under the enemy’s control [10]. • Hypersonic threats are weapon systems (missiles) that travel at speeds of more than five times the speed of sound (Mach 5, approximately 3,800 mph) and therefore reduce their exposure time to detection, tracking, and intercept. The primary categories include [11]: • Hypersonic glide vehicles (HGV) are launched from a rocket before gliding to a target; • Hypersonic cruise missiles are powered by high-speed, air-breathing engines, or scramjets, after acquiring their target.



The Naval Intelligence Reconnaissance and Surveillance Mission

7

• Swarm threats and tactics is the employment of weapons in coordinated attacks with unmanned weapon systems that saturate defenses to overwhelm sensing and defensive weapons; this complex integrated threat drives the need for integrated air and missile defense to deal with smaller signatures, mass raids, shortened response timelines, and longer ranges. These environmental and operational factors have driven the development of goals, objectives, and implementing strategies and principles for naval operations and ISR. The goal-to-implementation structure (Figure 1.3) for the U.S. Navy has been articulated in a series of documents that begin with two goals [12]. Maritime dominance is the operational goal that requires the achievement of information superiority. Superiority in the information domain drives the need for operational maritime domain awareness (MDA) which requires alldomain ISR. Figure 1.3 illustrates the key principles in the goals, operational objectives, and implementing MDA and ISR approach. The flow-down from an abstract goal (superiority of information) to practical ISR implementations is based on several assumptions: (1) better information (speed, accuracy, coverage) yields improved situation awareness, (2) this leads to improved decisionmaking, and (3) this results in greater lethality and impact. Maritime dominance requires the maritime force to achieve fundamental operational objectives in defense, offense, and battlespace awareness provided by all-domain ISR and the components of meteorology, oceanography,

Figure 1.3  Goals-to-ISR implementation flow-down.

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Naval ISR Fusion Principles, Operations, and Technologies

intelligence, cryptology, communications, networks, space, and electronic warfare (Table 1.2). Dominance requires the precise synchronization of these defensive, offensive, and ISR activities in time and across domains. For example, consider how a maritime operation may require ISR capabilities to: 1. Conduct persistent surveillance of a large ocean area by orchestrating a series of small satellites (smallsat) to locate maritime combatants. Concurrently coordinate unmanned air reconnaissance missions to locate dark vessels; 2. Monitor the electromagnetic spectrum for maritime radar and beacon emissions, communications, and monitor or penetrate cyber channels. Exploit information extracted from these channels; 3. Correlate and associate the information obtained across the space, cyber, electromagnetic, and physical domains to estimate the maritime situation (surface and subsurface vessels, aircraft, etc.), activities, and events; 4. Identify information gaps and potential threats. Issue collection tasks to orchestrate additional collection; 5. Place the information in the context of weather, the subsurface terrain and bathymetry, and the current geopolitical and maritime situation; 6. Estimate the current situation based on current observations and prior knowledge of the capabilities, patterns of operations, and intent of adversaries; 7. Develop and disseminate to commanders the timely and accurate common operating picture of Blue and Red (adversary) objects and projected behaviors and threats; Table 1.2 Key Operational Objectives of Maritime Superiority Aspect Awareness by all-domain ISR Defense Offense

Operational Objectives ·· Assure knowledge of status of distributed Blue forces; ·· Reduce uncertainty, locate, and track maritime objects (Red and neutral); ·· Know the maritime situation and predict feasible next situations; ·· Enable timely, accurate, and decisive command decisions. ·· Protect, defend, and recover own IE; ·· Increase the enemy’s friction by creating uncertainty. ·· Degrade, disrupt, deceive, deny, and exploit enemy’s IE to introduce fog; ·· Mass distributed forces decisively engage the enemy’s critical vulnerabilities to degrade and remove the enemy’s center of gravity.



The Naval Intelligence Reconnaissance and Surveillance Mission

9

8. Support commander decision-making to manage ISR collection as well as to plan the application of force using both nonkinetic and kinetic actions, and support C2 to plan, direct, monitor, and assess the employment resources. Navies perform many different kinds of operations and these influence the demands placed on ISR capabilities that are required to support operations. Representative naval operations and the demands on ISR systems are summarized in Table 1.3 to highlight the wide range of capabilities anticipated for surveillance and reconnaissance across a wide range of domains. Maritime operations are not performed alone, and navies often operate as an element of a joint force with air, space, and ground elements of other national or partner forces [13]. In joint operations, navies distinguish between organic ISR resources (those owned by the local force), networked ISR resources that are shared across a network of users, and national ISR assets that are elements of the national technical means (NTM) of intelligence collection [14]. The U.S. Navy specifically develops methods for tactical exploitation of national capabilities (TENCAP) to support tactical naval forces. TENCAP projects focus on maritime information superiority by [15]: • Improving procedures by which national systems products are made available to tactical forces; • Developing new methods for processing national system data into tactically useful information; • Formulating new concepts for using existing national systems in support of tactical operations and conducting tests and demonstrations to assess the effectiveness of these concepts; • Prepare tactical impact statements to influence the design of future national systems to assure the needs of tactical users receive appropriate priority.

1.3  Principles of Distribution, Automation, and Speed To achieve maritime information superiority on a global scale, ISR requires a wide distribution of sensing capabilities, automation of the sensing-collection process to provide speed from collection to response. In this section we introduce the principles of these three characteristics required for advanced ISR.

10

Naval ISR Fusion Principles, Operations, and Technologies Table 1.3 ISR Demands Across Representative Maritime Operations

Operations Surface warfare (SUW)

Operational Role Plan and direct surveillance of the maritime domain, interdict and conduct strikes by aircraft and missiles to destroy or neutralize enemy naval surface forces and merchant vessels. Air and Counter both air and missile threats missile by directing a combination of theater defense counterair and integrated air and missile (AMD) defense (IAMD) weapons. Antisubmarine Find, fix, track, target, and engage warfare enemy submarines; monitor, track, and (ASW) engage enemy submarines in port or transiting to operating areas as well as conduct active searches in operating areas. Mine warfare (MIW) Strike operations

Amphibious operations Maritime intercept operations (MIO) Maritime security operations (MSO)

Sea-based operations

Identify engagement opportunities to employ friendly mining capability, preclude adversaries from effectively employing maritime mining, and defeat the minefield. Destroy or neutralize targets ashore, including attacks against strategic or tactical targets, from which the enemy is capable of conducting or supporting air, surface, or subsurface operations against friendly forces. Embarkation and debarkation of landing forces from/to ships to land. Monitor, query, and board merchant vessels in international waters to enforce sanctions.

ISR Role ·· Conduct surface surveillance from space, air, or surface sensing. ·· Perform over-the-horizon target acquisition, tracking, and targeting. ·· Perform air surveillance in moving missile engagement zone (MEZ); ·· Acquire, track, and handoff high-speed targets. ·· Conduct broad-area maritime surveillance. ·· Apply persistent, national, and joint air, space (e.g., EO, IR, radar), surface, and subsurface (e.g., acoustic, nonacoustic) intelligence collection systems. ·· Apply ISR to detect mining ops, localize threat areas, and plan safe transit Q routes. ·· Apply ISR to guide mine-sweeping operations. ·· Apply space, air, human, or other sources to localize ashore activities and targets. ·· Track movements (e.g., convoys) and processes (e.g., fueling, weapon basing) that pose threats. ·· Identify noncombatant activities. ·· Perform overwatch ISR to identify threats complex amphibious ops. ·· Obtain departure and in-transit intelligence to track and identify suspicious vessels.

Establish conditions for security and ·· Coordinate with foreign intelligence protection of sovereignty in the maritime partners to identify activities, routes, domain (e.g., counterpiracy, counter and vessels. maritime-related terrorism, counter ·· Obtain departure and in-transit proliferation, transnational crime, illegal intelligence to track suspicious seaborne migration, or trafficking). vessels. ·· Provide intelligence for visit, board, search, and seizure (VBSS) activities. Establish an at-sea base for ships or ·· Provide supporting ISR for the seabase platforms to provide a scalable and and all operations from the base to mobile capability to exercise C2 or ashore and afloat activities. provide strike, power projection, fire ·· Provide intelligence to seabase support, and logistic capabilities. commanders on foreign responses.



The Naval Intelligence Reconnaissance and Surveillance Mission

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Table 1.3   (continued) Operations Counterdrug operations Foreign humanitarian assistance Riverine operations

Operational Role Support federal, state, and local law enforcement efforts to disrupt the transport and/or transfer of illegal drugs into the United States. Provide rapid national response in support of humanitarian and natural disaster relief operations. Deploy small, armed, patrol craft to provide maritime security in inshore, coastal, and riverine areas by patrol, surveillance, interdiction, and destruction of waterborne and land threats.

ISR Role ·· Coordinate with law enforcement intelligence operations, patrol to detect targets, monitor, and track at-sea traffic. ·· Perform overwatch ISR to identify threats to assistance activities, assets, and personnel. ·· Provide supporting intelligence by space, air, and land-based unattended sensor systems. ·· Provide supporting inshore human intelligence.

From: [16].

Distribution

The first attribute describes the structure of advanced MDA ISR systems. As DMOs move away from concentrated battle groups and MDA drives the need for extended surveillance coverage, sensors and weapons are distributed across a wide maritime area. Following the principles of network centric warfare (NCW), developed in the 1990s, sensors and weapons across all naval platforms operate on a network to provide an organic response to threats. The network concept is built on four fundamental principles that are applied to MDA [17]: 1. A robustly networked set of collectors, processors, and analysts improves information sharing; 2. Information sharing and collaboration to enhance the quality of information and shared situational awareness; 3. Shared situational awareness that enables self-synchronization across sensing platforms; 4. These, in turn, dramatically increase effectiveness—particularly the ability for ISR to respond to dynamic changes in the environment. NCW is inherently an information superiority concept of operations with the goal to achieve shared awareness, increased speed of command, higher tempo of operations, greater lethality, increased survivability, and a degree of self-synchronization. Sensors are shared so all platforms see a common operating picture; weapons are also shared so the network can respond dynamically as threats change [18].

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Naval ISR Fusion Principles, Operations, and Technologies

Automation

The scope and scale of MDA drives the need for higher automation in the ISR process. The scope, difficulty, and range of tasks requires higher dimensionality of sources and often requires MultiINT sources to be associated and combined to locate and identify targets such as vessels of interest. The global scale of MDA demands large areas of coverage and numbers of target vessels. These factors drive that need for increased automation and the orchestration of sensors and processing of information and deliver timely intelligence. Automation and autonomy offer two primary benefits: (1) improved safety, efficiency, and cost, and (2) expanded coverage beyond what human teams can accomplish, and increased speed, often beyond human performance for complex tasks. The MDA ISR challenge requires the latter to rapidly assimilate MultiINT maritime data, track and identify targets, identify threatening activities, and nominate targets. Speed

While the first two attributes dealt with the ISR structure, scope, and scale, this attribute deals with the need to operate at a speed faster than the adversary’s observe-orient-decide-act (OODA) loop—creating a lag in the adversary response. The distribution of sensors and introduction of a network increases the latency from sensing to fusion; this effect of distribution to achieve greater coverage must be overcome to maintain coverage and speed. “How much speed is required?” is the dominant operational question to implement distributed MDA and to conduct DMO. Speed is a relative requirement, and it is relative to operational sensing, network, and weapons performance, as well as the adversary speed of operations. Therefore, latency from sensor-to-decision maker (or shooter) is a key factor in the success of highly automated MDA systems.

1.4  MDA and ISR Operations in Conflict and Warfare ISR activities directly contribute to the goal of MDA defined earlier as the effective understanding of anything associated with the maritime domain that could impact the security, safety, economy, or environment. The United States emphasizes the global scope of its interest in the maritime domain, and acknowledges the MDA for the global maritime domain requires cooperation among security and commerce organizations and across nations [19]: This extends to the ISR capabilities that require sharing of global maritime information and intelligence to cover the world’s seas. The MDA challenge is significant in scope and scale, to include [19]: • Persistently monitor the global maritime domain:



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• Identified areas of interest; • Vessels and craft; • Cargo; • Vessel crews and passengers. • Maintain data on vessels, vessel tracks, facilities, and infrastructure; • Collect, fuse, analyze, and disseminate information to decision makers to facilitate effective understanding (e.g., detect anomalies, trends, and patterns, explain situations, predict behaviors, and warn of illegal activities and threats to maritime security); • Develop and maintain data on MDA-related mission performance. We further distinguish the MDA objectives of seeking what is observable and known (situational awareness), as well as what is anticipated or suspected (threat awareness) [20]. The MDA process has been characterized by a number of functional models developed to explain the method to collect data, analyze the data to develop maritime situation awareness, and further understand activities that provide threat awareness; we consider two well-known models here to illustrate the MDA process [21]. The OODA Loop

The OODA model developed by John Boyd provides a closed loop model from a command-and-control decision-making perspective [22]. Boyd emphasized the loop as a cycle to be operated at a tempo (speed) that outpaces the adversary. The stages of the cycle related to the MDA ISR process are: • Observe is the ISR process by which ISR sensors and sources collect information about the maritime environment. The observation process is guided by direction from the act process to collect information to improve the orientation and decision processes. • Orientation is the process that places the observed information in context, cross-correlating information, comparing information to mental models of maritime behavior, and projecting behavior into possible futures. Here, intuition and deliberate reasoning enable a situation to be estimated and alternatives to be considered. • Decide is the process of applying judgment to choose actions from alternative views of the situation, alternative actions, and the range of outcomes of those choices. Contextual factors (weather, geopolitical, logistics, etc.) and experience come to play in this process of judgment.

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• Act performs the operations in the maritime environment that respond to the current situation: (1) conduct additional surveillance and reconnaissance, (2) probe the environment to test hypotheses, (3) communicate with third parties, (4) engage maritime targets, and (5) conduct follow-up assessments. The results of actions are observed and the cycle reorients and makes new decisions. The detailed functions of the manual MDA ISR process are depicted in the OODA loop in Figure 1.4 to illustrate the use of the model as an organizing framework to describe the command-and-control functions and relationships of the MDA process. The example MSO activity receives tips about a suspicious vessel of interest (VOI) and creates a VBSS mission. The flow begins with observe and orient as a tip is received at the Maritime Operations Center (MOC) about a specific VOI. The tip information is assessed to determine credibility and the VOI is located (if possible) in the current and historical track knowledge base (SEALINK). In the decide phase, the MOC director guides the decision-making process that involves requests for information (RFIs) to assess the availability of resources (e.g., ships or aircraft to perform reconnaissance to locate the vessel, and ships available along the projected track to intercept and board). The MOC director develops the course of action (COA) and issues the orders to conduct reconnaissance, intercept, and board. The act phase carries out the COA and boards the ship, collecting biometric data from those onboard and sending the data ashore for crew identification and VOI analysis. Results can feed back to the decide phase causing additional actions. If the VOI remains suspect, it may be placed on the watchlist database for subsequent tracking. The Joint Directors of DoD Laboratories (JDL) Fusion Model

In the late 1970s, the JDL developed a model to describe the fundamental elements of the intelligence and fusion process, focusing on the core of the orient and decide elements of the OODA loop. The four-level model as originally envisioned is a fundamental description of four canonical elements of human cognition, applied to the intelligence data fusion process, a core element of ISR. The elements describe the human (manual) cognitive process as levels or degrees of activities that step through a cycle [24]. As described initially, it is a fundamental process model that describes human intelligence analysis, computer-aided analysis, medical diagnosis, forensic analysis, and many other endeavors—not just computer data processing. The original JDL model is based on (or, similar to) a fundamental model of cognition that has the stages (or levels) of sensation, perception of objects,



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Figure 1.4  Top-level MDA functions for VBSS mission in the OODA loop model [23].

perception of situations of objects over time, and cognition (reasoning) that constructs an understanding of the situation, followed by decision and action. The highest-level model distinguishes System 1 cognition (intuition: fast, performed in parallel, automatic and effortless, slow learning, and emotionally influenced) and System 2 cognition (reasoning: slow, performed in parallel, controlled and requiring effort, flexible, and neutral) [25]. The basic four JDL levels are:

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• Level 1, entity assessment: Estimation and prediction of entity parametric and attributive states (i.e., of entities considered as individuals); • Level 2, situation assessment: Estimation and prediction of the structures of parts of reality (i.e., of relations among entities and their implications for the states of the related entities); • Level 3, impact assessment: Estimation and prediction of the utility/cost of signal, entity, or situation states including predicted impacts given a system’s alternative courses of action; • Level 4, performance assessment: Estimation and prediction of a system’s performance as compared to given desired states and measures of effectiveness. This also includes the feedback to manage resources (levels 1–3 processing and sensors/sources in the closed loop). Figure 1.5 compares a simple cognition model and the closed-loop JDL model. The robotics discipline has adopted a similar model, of course, because this is quite fundamental, and represents the fundamental elements of thinking and autonomy. The cognitive functions focus sensing and processing to reduce uncertainty about the situation and threats. More recent revisits to the model have added new functions: (1) a level 0 (signal/feature assessment) that precedes level 1 and combines raw signals or features from multiple sources to detect and identify objects and their states, (2) a level (or function) 5 (user refinement) that is the human machine interface (HMI), and (3) a function 6 (mission management) that includes the mission actions beyond sensing and collection [26]. Waltz introduced the complementary nature of machine learning (or ML, referred to as data mining in the 1990s) and data fusion to illustrate how inductive ML processes discover and

Figure 1.5  A cognitive model compared to the JDL fusion model.



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refine models of targets for deductive fusion processes to detect those targets in operational data (Figure 1.6) [27]. The JDL model has provided a basis for distinguishing the processing stages and implementing algorithms for five decades and remains the standard for those in the ISR discipline. The JDL model is adopted as the framework used throughout this book as the common reference for ISR processes.

1.5  About This Book This text is designed to help naval officers and technical professionals to understand maritime threats and advanced naval ISR to support DMO to counter complex threats. The book describes the complexity of achieving seabed-tospace sensor coordination on a distributed network, the fusion of ISR information, and decision-making support. We focus on the methods to apply advanced technologies in fusion: sensing orchestration, distributed data correlation and association, data combination, and distribution. The book is suitable for naval officers in the ISR, intelligence, space, ASW, electronic warfare (EW), cyber, and surface warfare disciplines who seek an in-depth understanding of advanced ISR operations and technologies. It is designed to provide a comprehensive background for naval and industry managers and engineers planning and developing advanced naval systems—subsurface, surface, air, cyber, and space.

Figure 1.6  Complementary ML and data fusion processes. (From: [28]. Figure reprinted with permission from IEEE.)

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The book is organized to introduce the new maritime threats, policies, and operations that will place demands on future ISR systems before describing the methods and technologies that will be required to meet future naval threats. The chapters are organized in a framework (Figure 1.7) that first introduces the mission, role of ISR in Chapter 1, and the challenges in maritime environments in Chapters 2–5. The emphasis in this first section is in the challenges introduced by all-domain ISR and the distribution of sensing and operational assets. The second half of the book, Chapters 6–10, describe the technical operation of distributed ISR and the necessary technologies to enable highly automated, distributed sensing across all domains. At appropriate places in the book, example scenarios are provided to illustrate the operations and challenges. We also provide throughout quantitative measures of operating parameters, typical performance, and effectiveness of the scenarios provided. Chapter 2 introduces the demand for ISR to support coordinated naval operations in all domains from seabed to space. The naval operations in the domains of seabed, subsurface, surface, cyber, air, and space are introduced with a focus on how they each contribute to MDA. The demands for ISR across these domains are explained, as well as the challenge of delivering a timely and comprehensive awareness to provide commanders with an understanding of an adversary’s coordinated operations. An all-domain operations scenario is provided to illustrate how all domains can conceptually be integrated to work together. Chapter 3 explains the recent doctrine of DMO and its relationship to demands on ISR for widely distributed sensing and force elements, such as DMO tactics, architecture, and elements. The necessity of a communication grid to connect distributed nodes, node data storage, processing power, and technology stacks are explained as well as the need for data and analytic strategies to

Figure 1.7  Organization of the chapters move from mission to ISR fusion technologies.



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enable rapid and accurate information to enable decisive action. The chapter concludes with a DMO scenario to illustrate the functions and operations in the concept. Chapter 4 provides an overview of the current U.S. Navy ISR fusion architecture as a baseline reference and then projects the C2 challenges that drive the ISR requirements. The chapter provides an introduction to maritime ISR systems and a baseline architecture for a more centralized sensor and information fusion. Chapter 5 explains the challenges to perform all-domain ISR, focusing on three challenge areas. The first challenge is reconciling the spatial distribution of platforms, associating observations and latency across distributed networks. The second is utilizing sensor observations with low temporal sample rates and highly dynamic targets (e.g., fleeting, hypersonic, and swarming). The third challenge is achieving the persistence required for surveillance and the accuracy required for C2 and fire control. The second section of the book details the systems and technologies necessary to enable more distributed operations. Chapter 6 introduces the functional requirements for naval MultiINT fusion systems and the technologies that implement them. The chapter briefly introduces the JDL model for organizing naval ISR functions. Then the core functions of object and situation refinement, threat refinement, and resource orchestration are explained. The chapter focuses on the necessity for orchestrating distributed resources to effectively support DMO. Chapter 7 explains the necessity of adaptive networks to route sensor data across a grid of nodes to users: other sensors, fusion nodes, and command nodes that perform operational decision-making. The principles of network operation and security are detailed for the grid. Chapter 8 introduces the emerging role of AI, automation, and autonomy in naval ISR systems. The rationale and elements of automation of the ISR network are explained, focusing on automation and intelligence in sensing and sensemaking. An all-domain automated operation scenario illustrates the contributions of automation and autonomy to DMO. Chapter 9 focuses on the new global maritime reconnaissance and surveillance opportunities provided by distributed space sensing by constellations of smallsat constellations, particularly the revolutionary changes in maritime transparency. The chapter illustrates the game-changing contribution of persistent maritime surveillance and its impact on DMO. Chapter 10 concludes the book with an overview of future technologies that will enable even more advanced capabilities in all-domain ISR.

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Endnotes [1] Joint Publication 2-01, Joint and National Intelligence Support to Military Operations, U.S. DoD Joint Chiefs of Staff, July 5, 2017. [2] Law of the Sea: A Policy Primer, “Maritime Zones” The Fletcher School of Law and Diplomacy, 2017, Chapter 2, https://sites.tufts.edu/lawofthesea/chapter-two/. [3] The IE and its subset and cyberspace are defined by the DoD in Joint Publication (JP) 1-02, the Department of Defense Dictionary of Military and Associated Terms. Notice that the definitions distinguish between three fundamental domains: the physical domain, an information domain (which includes cyber), and the human cognitive domain. See the “DoD Strategy for Operations in the Information Environment,” June 2016. [4] “A Design for Maintaining Maritime Superiority,” Version 2.0, U.S. Navy, December 2018, p. 6. [5] Defined in “Amendments to the International Aeronautical and Maritime Search and Rescue (IAMSAR) Manual,” International Maritime Organization, T2-OSS/1.4 MSC.1/ Circ.1367, May 24, 2010. [6] “Navy Strategy for Achieving Information Dominance, 2013; The U.S. Navy’s Vision for Information Dominance,” 2010. See also “Information Superiority Vision,” Department of the Navy, February 2020. [7] “Surface Force Strategy: Return to Sea Control,” Surface Force Command, 2018. [8] The concept of great power competition is articulated in the U.S. Department of Defense, “National Security Strategy of the United States of America,” Washington, D.C., December 2017. [9] Clark, B., et al., “Restoring American Seapower: A New Fleet Architecture for the United States Navy,” Center for Strategic and Budgetary Assessments, 2017, p. 12, https://csbaonline.org/uploads/documents/CSBA6292-Fleet_Architecture_Study_REPRINT_web. pdf. [10] Krepinevich, A. F., “The Military-Technical Revolution: A Preliminary Assessment,” Center for Strategic and Budgetary Assessments (CSBA), 2002, p. 1. [11] Congressional Research Service, Hypersonic Weapons: Background and Issues for Congress, R45811, updated March 17, 2020. [12] Above these Navy documents, the United States has Presidential Policy Directive 18, “Maritime Security,” (August 2012). From this directive derives a National Security Policy Document (NSPD-41) and a Homeland Security Policy document (HSPD-13) that define U.S. maritime security policy (open-ocean and homeland coastal-ports, respectively). These national policy level documents then flow to a National Maritime Security strategy that guides Navy goals, operational objectives, and implementing strategies. The Department of Defense Directive 2005.02E, “Maritime Domain Awareness (MDA) in the Department of Defense,” defines DoD policy and assigns responsibilities. [13] For example, see the Department of Defense, Joint Publication 3-0: Joint Operations, Washington, D.C., 2006, p. IV–26.



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[14] National technical means (NTM) referred to overhead satellite technical collection systems in the 1970s though they can be applied to other special collection capabilities. See Smith, C. E., “Watching the Bear: Essays on CIA’s Analysis of the Soviet Union,” Chapter IV, G. K. Haines, and R. E. Leggett (eds.), “CIA’s Analysis of Soviet Science and Technology,” CIA Center for the Study of Intelligence, 2007. [15] Navy Tactical Exploitation of National Capabilities (TENCAP) Unclassified Solicitations (FY 2015–2020) DISC Working Documents (2020). Naval Digital Integration Support Cell. [16] Operations in the table are based on Maritime Operations Joint Pub 3-32, Department of Defense, June 8, 2018. [17] Cebrowski, A. K., and John H. Garstka, “Network Centric Warfare: Its Origin and Future,” Proc. Naval Institute, Vol. 124/1/1,139, January 1998. See the fundamental literature on NCO at http://www.dodccrp.org. [18] Garstka, J., F. P. Stein, and D. S. Alberts, “Network Centric Warfare: Developing and Leveraging Information Superiority,” Washington, D.C.: DOD C41SR Cooperative Research Program, February 2000, p. 2. [19] See “National Strategy for Maritime Security,” September 2005, and “National Plan to Achieve Maritime Domain Awareness for the National Strategy for Maritime Security,” United States, October 2005. [20] “Navy Maritime Domain Awareness Concept,” U.S. Navy Chief of Naval Operations, May 2009. [21] In addition to the two models described, two other models should be noted: (1) The sensehypothesize-option-respond (SHOR) model was introduced in 1981 by Joseph Wohl (Wohl, F. G., Force Management Decision Requirements for Air Force Tactical Command & Control, IEEE Transactions in Systems, Man and Cybernetics, SMC11, 9, pp. 618–639 (September 1981)), and (2) the task-collect-process-exploit-disseminate (TCPED) model that describes the sequence of activities performed in intelligence collection-to-reporting (TCPED is a technical representation of the more general intelligence cycle model). [22] Osinga, F., “Science, Strategy and War: The Strategic Theory of John Boyd,” Netherlands: Eburon Academic Publishers, 2005. [23] Adapted from the VBSS example provided in Hutchins, S. G., et al. “Enhancing Maritime Domain Awareness,” Proc. of 13th ICCRTS: C2 for Complex Endeavors, 2008. See also “Maritime Domain Awareness: Assessment of Current Status,” Proc. of 14th ICCRTS: C2 and Agility, 2009. [24] “Functional Description of the Data Fusion Process,” Joint Directors of Laboratories, 1991. [25] Readers familiar with the Kahneman-Tversky cognition models distinguish between two models, or systems of thought: fast (intuitive) reaction and slow (deliberative analysis). Kahneman, D., Thinking, Fast and Slow, NY: Farrar, Straus and Giroux, 2011. Kahneman, D., “A Perspective on Judgment and Choice,” American Psychologist, Vol. 58, No. 9, September 2003, pp. 697–720.

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[26] Llinas, J., C. Bowman, G. Rogova, A. Steinberg, E. Waltz, and F. White, “Revisiting the JDL Data Fusion Model II” International Data Fusion Conference, July 2004. For a more recent view, see Blasch, E. P., É. Bossé, and D. A. Lambert, High-Level Information Fusion Management and System Design, Norwood, MA: Artech House, 2012. [27] Waltz, E. L., “Information Understanding: Integrating Data Fusion and Data Mining Processes,” Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, Vol. 6, 1998, pp. 553–556.

2 Principles of Operations from Seabed to Space Although the concept of DMO represents a new thrust in today’s naval warfighting scenario, the idea has developed over several decades following technological advances and historical events. In the 1950s, the first tactical data link was developed and deployed in the United States in response to the development of Soviet antiship cruise missiles. The data link had the advantage of reducing latency as well as the time to observe, orient, decide, and act, thereby enabling the warfighters a quick reaction time [1]. In the late 1970s, the Aegis Combat System, an advanced C2 and weapon control system, was developed which enabled combat systems onboard a ship to work together to track and guide weapons. In the 1990s, a cooperative engagement capability (CEC) was developed to enable raw sensor data to be shared in real time with other combat units. This sensor data sharing was a transformative step that allowed ships to respond to a threat based on data detected by another ship’s radar if they were part of the CEC network. Former Rear Admiral Rodney P. Rempt, director of theater air and missile defense on the Navy staff, envisioned a future tactical grid as an agnostic network of weapons and sensors, controlled by a specific number of nodes, with no restrictions on the location or deployment site of the weapons, sensors, or controlling nodes [2]. The former chief of naval operations (CNO), Admiral John M. Richardson emphasizes in the 2016 report “A Design for Maintaining Maritime Superiority” the need to “adopt a strategy that allows sea control and maritime superiority to address the threat rising from Russia and China and also to address ‘blue-water’ scenarios far from land and power projection ashore in a highly ‘informationalized’ and contested environment” [3]. Although this event did not directly call for DMO, it has certainly set the 23

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stage and presented a challenging problem to which DMO is the only solution. The CNO was confident that even though tough choices were made due to budget restrictions, innovative and inspiring ideas would emerge as a result of these choices. Indeed, in 2017, former Vice Admiral Thomas S. Rowden from the Naval Surface Forces presented his “Surface Force Strategy, Return to Sea Control” document that discussed the concept of distributed lethality (DL) that aims at achieving sea control especially in the surface domain. The DL concept is based on the following pillars: • Increasing the potency of single warships by networking the firing capability; • Geographically spreading out over a wide area the offensive capability; • Enhancement of combat capability through the allocation of resources to surface platforms [4]. In 2018, the U.S. Harry S. Truman Carrier Strike Group presented the concept of DFE whose goal was to “more flexibly use ready forces to shape proactively the strategic environment while maintaining readiness to respond to contingencies and ensure long-term warfighting readiness” [5]. The chairman of the Joint Chiefs of Staff, General Joe Dunford, and Defense Secretary Jim Mattis pushed implementation of DFE to allow the military to quickly deploy forces while ensuring that the military infrastructure remained agile and less predictable [6]. The DMO concept shares the key tenets with DL and carries the principles that form the basis of DFE, except that it extends the maritime domain to encompass all domains including air, subsurface, space, and cyber warfare. A primary goal of DMO is allowing operational commanders the ability to treat the entire fleet assets under their control as a single weaponized system. The individual systems of varying capabilities are integrated to provide both a unit defense and a collective defense. The integrated package unites diverse and disparate weapons, technologies, sensors, platforms, and systems into a single system with capabilities exceeding the sum of the parts. In addition to engaging and defeating multidimensional threats across all domains in the maritime environment, the single united weapons system can also conduct offensive strikes and other fleet engagements [4]. While the U.S. Navy has been open about its conceptual strategies and operations over the past decades, the needs and technical solutions described throughout this book are of interest to navies of the world that subscribe to the conventions of international security on the seas. These nations participate in organizations and alliances that cooperate, establish standards, and exchange thought on maritime security. The interested reader should monitor



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international maritime policy and technology sources on the current views of international navies, for example: • The Center for International Maritime Security (CIMSEC) publishes reader-submitted content specifically on international maritime peace and security. CIMSEC has 20 international chapters, over 3,000 members, subscribers in 60 countries, and has published many contributed technical articles on aspects of DMO. • The International Maritime Organization (IMO) provides policy guidance and “machinery for cooperation among Governments in the field of governmental regulation and practices relating to technical matters of all kinds affecting shipping engaged in international trade; to encourage and facilitate the general adoption of the highest practicable standards in matters concerning maritime safety, efficiency of navigation and prevention and control of marine pollution from ships” [7]. • NATO Allied Maritime Command [8] and the NATO Science and Technology Organization [9]. • Australian Defence Science and Technology Group reports on maritime and ISR technologies [10].

2.1  Maritime Awareness MDA is defined in the National Strategy for Maritime Security as “the effective understanding of anything associated with maritime domain that could impact the security, safety, economy, or environment of the United States” [11]. MDA is a tool that allows navies, coast guards, and other maritime-based organizations to monitor the oceans, assess threats, and ensure that waters are safe and free from any criminal activities. The air and ground domains have designated check points and well-defined infrastructure and support systems as well as rules and policies. Although developed over centuries, the maritime domain has always lacked structure and cohesion of operation, making open oceans even more vulnerable. Just weeks after 9/11, a 50 ft–long boat offloaded over 200 illegal immigrants right in downtown Miami, thereby exposing the vulnerability of the security of the nation as well as putting its economic and transportation systems at risk [12]. The inability to predict or keep custody of targets of interest before they become a threat along with increasing concern about terrorism, drug smuggling, human trafficking, and piracy, resulted in placing MDA and maritime security high on several nations’ security agendas in the early 2000s [13]. MDA aims at improving ocean transparency by learning ship traffic trends, detecting illegal or nefarious activities, and identifying any anomalies

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or deviations from normal patterns. Once an anomaly is detected, a situation threat assessment is performed to assign a threat value to the situation which enables decision makers to act as early and as distant from home as possible. However, an efficient MDA strategy involves a continuous monitoring of vessels within an area of interest or globally, as well as an improvement in the ability to collect, fuse, analyze, and disseminate information to decision makers as shown in Figure 2.1. The process of achieving MDA involves persistent monitoring of entities in the global maritime domain ranging from keeping custody of the different types of vessels (fishing vessels, leisure, cargo, military, etc.), to monitoring vessel crews and passengers, to learning trends in the maritime routes and keeping track of friendly and foe forces, to gaining situational awareness about ports and other maritime infrastructures. This process involves the following steps in order: 1. Data collection: MDA requires the aggregation of data from different sources on people, vessels, facilities, infrastructure, and so on. 2. Data fusion: the availability to collect geospatial datasets makes monitoring the maritime domain feasible especially when integrated with other forms of intelligence (signal intelligence (SIGINT), geospatial intelligence (GEOINT), automatic identification system (AIS), and open-source intelligence (OSINT)).

Figure 2.1  Conceptual model of the elements of the intelligence process [14].



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3. Data analysis: Data analysis conducted through machine learning algorithms and artificial intelligence promises a faster and more autonomous tool to identify anomalous behaviors, trends, and patterns and to predict behaviors. 4. Data dissemination: Once the data collected from different sources is analyzed, decision makers are presented with an effective understanding of the maritime domain either on a regional or global level (depending on the mission) thereby enabling strategic, operational, or tactical response. Intelligence Source Terminology Throughout this book we use standard intelligence terminology to distinguish the many categories of sources, collection methods, and analytic processes. The major recognized terms and acronyms are provided as follows: SIGINT is the exploitation and analysis of data derived from electronic signals and systems used by foreign targets, such as communications systems, radars, and weapons systems. ELINT (electronic intelligence information) is derived primarily from electronic signals that transfer data, but do not contain speech or text. TechELINT is ELINT that focuses on describing the signal structure, emission characteristics, modes of operation, and weapon system associations of emitters. OpELINT is ELINT that focuses on locating specific ELINT targets to report the electronic order of battle (EOB) and threat assessments (or, TacELINT). COMINT is the collection and processing of foreign communications (excluding open sources of communications such as radio and television broadcasts) for the purpose of extracting information of value to intelligence. Cyberspace operations (CO) is the employment of cyber capabilities where the primary purpose is to achieve objectives in or through cyberspace. Cyber intelligence operations include: Cyber operational preparation of the environment (C-OPE) is the collection and analysis of information that maps the cyber environment (e.g., networks, nodes, or channels); Cyber ISR is the collection and analysis of cyber sources to support offensive and defensive cyberspace operations.

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GEOINT is the exploitation and analysis of imagery and geospatial information to describe, assess, and visually depict physical features and geographically referenced activities on the Earth. GEOINT consists of imagery, imagery intelligence, and geospatial information. HUMINT (human intelligence) is the collection and analysis of information obtained from human sources. ACINT (acoustic intelligence) is the collection and analysis of acoustics signals to locate, identify, and track maritime vessels (generally from active or passive SONAR systems) or overhead aircraft. MASINT (measurement and signature intelligence) is the collection and analysis of data obtained by quantitative and qualitative analysis of data (metric, angle, spatial, wavelength, time dependence, modulation, plasma, and hydromagnetic) derived from specific technical sensors for the purpose of identifying any distinctive features associated with the emitter or source. OSINT is the analysis of publicly available information (PAI) from many sources, including foreign news sources, social media, and mobile device location data.

ISR is a military operation that plays a pivotal role across all domains (air, land, seas, space, and cyberspace) and enables the execution of MDA functions. Its goal is not only to help decision makers and stakeholders “anticipate change, mitigate risk, and shape outcomes,” [15] but it also has a great influence on how missions and operational exercises are planned to defeat the adversary across all domains. The U.S. DoD defines ISR as “an integrated operations and intelligence activity that synchronizes and integrates the planning and operation of sensors, assets, and processing, exploitation, and dissemination systems in direct support of current and future operations” [16]. ISR platforms, that includes satellites, ships, humans, UAVs, UUVs, and planes, collect specific information using a wide range of sensors about an area of interest. The collected data is analyzed with the help of AI and ML algorithms to not only produce intelligence reports that support operational needs to defeat the adversaries but also to protect the nation and its forces. Figure 2.2 illustrates the breadth of ISR across all domains from seabed to space, including the cyber domain, to detect objects of interest and predict their behavior within a battlespace. This effort necessitates the fusion of all types of information (SIGINT, GEOINT, MASINT, PAI, and HUMINT) across all domains and requires the collaboration of all forces operating across the domains as well as working with allies to ensure timely information flow from one service agency to another for a successful execution of missions. This concept is referred to as the Joint All-Domain Operations (JADO) which is defined as “operations conducted across multiple domains and contested spaces to overcome an adversary’s (or enemy’s) strengths by presenting them



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Figure 2.2  ISR capabilities across all domains [17].

with several operational and/or tactical dilemmas through the combined application of calibrated force posture; employment of multi-domain formations; and convergence of capabilities across domains, environments, and functions in time and spaces to achieve operational and tactical objectives” [18]. One of the challenges that faces this concept is data handling. Standardizing the data across services or even within each service can be challenging because each type of data is stored in a specific format. Data is also stored at different classification levels which makes data sharing a challenging task. The lack of a standard taxonomy renders information exchange between different military and intelligence services difficult unless a translation software is used to present the data to a specific agency in its own lexicon. The availability of large amounts of data generated across all ISR domains necessitates the use of AI/ML algorithms where humans and machines work together to process all this information. However, one challenge in using AI/ML technology is the ability for decision makers to trust the performance of the developed algorithms, which can only be gained after long periods of test and development. This trust is further enhanced with the algorithm’s demonstrated ability to detect drift, deception, and black swans. On September 30, 2020, the deputy secretary of defense, Mr. David L. Norquist, presented a DoD data strategy document that focuses on the need to work with stakeholders in the operational domain and to “…treat data as a weapon system and manage, secure, and use data for operational effect” [19]. Data needs to be: • Visible: Users should be able to easily locate the data;

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• Accessible: Users should understand the content of the data and the context in which it was taken; • Linked: Users should understand the relationships between different data elements; • Trustworthy: Users should trust the data in making decisions; • Interoperable: Users across all domains should possess a common understanding of the data; • Secure: Users should have confidence that the data is only available to authorized users. To ensure that the data satisfies the abovementioned goals, the following capabilities need to be developed: 1. Architecture: Build an architecture that allows access to the data but requires that capabilities be able to adjust in response to technological and data requirement changes; 2. Standards: Employ standards for the managing, representing, sharing, and utilizing datasets; 3. Governance: Data governance provides guidelines, processes, tools, metrics, and policies needed to manage the data sets from the time they are acquired to the time they are disposed; 4. Talent and culture: A shift towards a culture that revolves around the data requires providing the necessary skills and training for the workforce to take advantage of this new system to make data-based decisions, create policies, and develop processes. Some of the challenges facing MDA are: 1. Uncooperative vessels: Some vessels prefer to stay anonymous to avoid detection and surveillance for several reasons (military, drug distribution, piracy, etc.). It is therefore a challenge to collect and analyze information, which is often very sparse, on such vessels. 2. MDA enterprise establishment: Implementing a maritime enterprise capability that functions in a cohesive way to share information, analyses, and maintains a situational awareness at all stages of a mission is a major challenge. To determine anomalous behavior and understand all maritime activities, different branches of the military need to cooperate within their services as well as with allies, commercial companies,



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and civilians. A successful MDA involves establishing an enterprise at the international level. This necessitates the development of [20, 21]: • MDA collaborative platform; • Network to keep the MDA community connected: It is important to keep MDA data accessible, agile, and flexible to all users to address the real-time needs of the fleet to meet mission requirements and support decision makers; • Quality control and quality assurance of MDA information: Protecting information collected from adversarial attacks is crucial to ensure integrity and confidentiality of information. 3. Personnel information: For a successful MDA execution, it is important to maintain security information on all personnel involved in the maritime domain. This becomes challenging when considering all involved parties in the maritime domain ranging from operators of fishing vessels, cargo, and cruise lines to passengers. 4. Data acquisition: Situational awareness during peace and war times requires detailed information to be gathered. This can be achieved through acoustic, optical, and electromagnetic source methods using different platform-based sensors (unmanned vehicles, satellites, sonobuoys, etc.). The data needs to address the need for real-time analyses and dynamically changing mission plans to meet the warfighter needs. 5. Resource orchestration: Allocation of resources to meet increased need for surveillance on a specific area of interest calls for the design of an agile and flexible infrastructure that allows for reallocation of resources in a short time notice.

2.2  Naval Subsurface and Seabed Domains In the subsurface domain, platforms include both manned and unmanned systems that leverage stealth in their execution of the ISR mission. Modern navies deploy either manned nuclear or diesel-electric submarines, or a mix, classified as attack, hunter-killer, or multipurpose/mission submarines. Typically, their primary missions are special operations, ISR, and with the incorporation of guided missiles, strike. Their ability to covertly collect intelligence and maintain station for extended periods of time make them premier ISR platforms. Table 2.1 summarizes typical attack submarine ISR capabilities, the associated phenomenology, and mission of each.

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Naval ISR Fusion Principles, Operations, and Technologies Table 2.1 Typical Attack Submarine ISR Capabilities

ISR System SONAR system (active, passive)

Phenomenology Mission Propagation of Detection, acquisition, sound energy identification, and localization of acoustic emissions

Electronic warfare system, electronic support measures (ESM)

Propagation of radio frequency (RF) energy

Detection, acquisition, identification, and localization of RF emissions

Examples Thales UMS-3000 (UK), AN/BQQ-10 A-RCI (U.S.), and SONARtech Atlas AI&R (Germany) AN/BLQ-10 (V) (U.S.)

Unmanned underwater vehicles (UUV) also leverage stealth in the execution of the ISR mission, but with the added benefit of autonomy with no risk to human life. Further, they extend reach into denied areas, and enable missions in water too shallow or otherwise inaccessible for conventional platforms [22]. Current and proposed ISR missions include persistent and tactical intelligence collection (signal, electronic, measurement, and imaging intelligence, meteorology, and oceanography), chemical, biological, nuclear, radiological, and explosive (CBNRE) detection and localization, near-land and harbor monitoring, deployment of leave-behind surveillance sensors or sensor arrays, specialized mapping, and object detection and localization [23]. UUV systems can be categorized into four classes according to their size. Sizes range from man-portable vehicles with payload capacities of less than 0.25 ft3 to large vehicles with payload capacities of 15 to 30 ft3 with the possibility of external stores. Light (LWV) and heavy weight vehicles (HWV) find utility as well. Based on size, weight, and power constraints, each class of UUV offers a unique contribution to the ISR mission as summarized in Table 2.2 [23].

Table 2.2 Classes of UUV, Their Nominal Levels of Performance, and ISR Applicability

Class Manportable LWV HWV Large

From: [23].

Endurance Diameter Displacement High Hotel (inches) (lbs) Load (hours) 3–9 400

~500 100 nanosatellite vehicles; ·· LEO; 500-km sun synchronous. ·· >6 clusters of 3 formationflying satellites; ·· LEO, 575 km.

Sensor Parameters ·· C-band; ·· High-resolution 1m × 3m (spotlight mode); ·· Medium resolution ship detection mode resolution 50m × 50m; swath width 350 km. ·· X-band; ·· Resolution in modes strip = 3m, spot = 1m, and scan = 15m. ·· X-band; ·· Highest resolution 50 cm.

·· High-resolution 30–50 cm optical imagery. ·· Color and near-infrared (NIR) imagery; ·· 90-cm resolution. ·· Bayer-masked chargedcouple device (CCD) camera; ·· Ground sampling distance (Nadir) ranges between 2.7m to 4.9m; ·· Spectral band: red, green, blue, NIR; ·· 3m multispectral image resolution. AIS receivers are aggregated to provide continuous vessel tracking via subscription service SDRs for maritime AIS and aviation ADS-B tracking. SDRs geolocate VHF marine radios, UHF push-to-talk radios, maritime radar systems, AIS beacons, and L-band satellite devices.

Note: SDR is software defined radio; ADS-B is automatic dependent surveillance-broadcast.

periodic AIS signals [42]. AIS automatically provides vessel information to other ships and coast authorities fitted with an AIS transponder

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that operates in the very high frequency (VHF) mobile maritime band. The data provided by AIS is spatiotemporal data that consists of latitude, longitude, course-over-ground, speed-over-ground, vessel identifier number, navigational status, time stamp, and so on. AIS reporting contributes to vessel tracking for MDA as well as for collision avoidance, and search and rescue. AIS is a cooperative system that requires the cooperation of vessels and therefore does not account for dark vessels. Those are vessels that evade tracking by turning off the AIS transponder or by providing incorrect ID codes or locations. Of course, maritime radars and other emitters on ships provide potential sources for detection and location by RF constellations that use interferometric methods to localize the emitter location [43]. In each case constellations of multiple satellites with sensors contribute maritime information; the correlation and combination of this data from all three modes (data fusion) can enhance the ability to monitor and track the movement of individual vessels. While we provide examples of commercial satellite constellations, dedicated military or naval constellations can directly provide this information for intelligence, surveillance, reconnaissance, and targeting (ISRT).

2.5  Naval Cyber Cyberspace, often referred to as the fifth operational domain, refers to a domain that hosts interconnected networks of data and the information contained within those networks. As navies operate in a global environment, they are exploiting large amounts of data collected by systems deployed from seabed to space, they are fusing all this information, they are sharing it, and they are communicating it through sophisticated networks to achieve situational awareness and military dominance. As a result, the military does not only depend on the cyberspace domain to execute specific cyberspace operations, but it relies on cyberspace for the success of military missions and operations in the sea, land, air, and space domains. The cyberspace is characterized by the following [48]: • Innovation: Cyberspace evolves in response to advances in technology, system processes, and architectures to produce capabilities that meet the needs of the military. Therefore, it requires constant vigilance and awareness of the most recent technological breakthroughs that affect the domain.



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• Complexity: One way to understand part of the complexity of the cyberspace is by focusing on the geophysical hierarchy of all domains. The surface of Earth (land domain) is surrounded by the maritime domain, which is surrounded by the air domain, which in turn is surrounded by the space domain. However, the cyber domain is embedded in all four domains and mission success in all these domains depends on a successful cyberspace operation. • Interdependence: Cyberspace is formed by an interconnection between information, and the hardware and software systems that store, analyze, and transmit the data. • Volatility: Because the cyberspace keeps changing, it can be a challenge to ensure effectiveness of some cyber operations and ensure that vulnerabilities are not introduced because of such changes. • Speed: Information within the cyber domain moves quite rapidly to allow commanders and warfighters to make decisions and conduct operations. The cyber domain becomes increasingly effective as the speed for processing all the collected data and analyzing it to make operational and strategic decisions improves. Cyber superiority is achieved by realizing an infrastructure that: • Provides freedom to the stakeholders to access, analyze, manipulate, and communicate information between services and across all domains. • Denies access of the cyberspace to adversaries during specific times and at certain locations. • Protects the network by ensuring the security of each of the different layers that form the cyberspace domain [49]: 1. The physical layer: This layer consists of physical and geographical components, the hardware backbone of cyberspace. A prominent and obvious example of a physical layer is the fiber-optic network of cables including undersea cables. Another example includes the constellation of satellites. 2. The logic layer: This layer involves the data in the state of rest as well as in a state of transmission. It is the cyberspace analog to the central nervous system. The logic layer is responsible for retrieving files and for sending and receiving messages. Much like the human brain, the logic layer is where decisions are made. Two key elements of the logic layer are domain name servers (DNS) and Internet Protocols (IP).

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3. The information layer: This is the layer that acts as persona part of cyberspace and includes examples from the internet, such as websites, chats, emails, photos, documents, and apps. It represents individuals and organizations, both real and fictitious. Without the previous two layers, the information layer cannot function. • Offers the ability to perform offensive operations, the ability to gain information with high intelligence value by: 1. Attacking the cyberspace by performing actions or manipulations that create noticeable denial effects; 2. Exploiting the cyber domain of adversaries to gain intelligence. Naval fleets operating afloat in the maritime environment coordinate cyber activities with ashore cyber units to conduct the operations in three areas across the global network (Table 2.7). Because the afloat cyber operators conduct activities that are closer to adversary fleets they may have enhanced capabilities to access and monitor target networks. Cyber ISR operations include a wide range of monitoring activities on internet networks, dark networks, and social media channels to perform threat assessments for afloat commanders.

2.6  A Seabed-to-Space Scenario In this section we provide a scenario that describes the roles of the various naval platforms and sensors in a representative situation to illustrate how networked sensors provide ISRT to enable naval operations. The scenario is designed to demonstrate, in a near-realistic situation, the many variables that come into play to conduct naval operations that demand excellent ISRT and maritime awareness. • The scenario is a sequence of imagined events that unfold in a naval operation; • A naval operation is a naval action (or the performance of a naval mission) that may be strategic, operational, tactical, logistic, or training. It is a process of carrying on or training for naval combat to gain the objectives of any battle or campaign [51]. The brief scenario is represented by the following elements: • Background and situation are the starting geopolitical and military (naval) events that drive the need for naval operations;



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Table 2.7 The Categories of Naval Cyber Operations Preparation and ISR ·· C-OPE; ·· Network ISR.

Defensive ·· Defensive counter cyber (DCC); ·· Proactive NetOps, defensive countermeasures. ·· Map target networks; ·· Follow established defense ·· Assess adversary cyber order policies, techniques, tactics, of battle; and procedures (TTPs); ·· Monitor threatening conduct operations to: activities; ·· Monitor and guard ·· Provide indications and processes to prevent and warning; delay attackers. ·· Develop targeting ·· Report and restore cyber information, perform strategic incidents. target development.

Offensive ·· Offensive cyberspace operations (OCO); ·· Offensive counter cyber (OCC). ·· Apply offensive tools, tactics, and procedures; ·· Conduct offensive operations as required under proper authorities; ·· Synchronize ashore-afloat cyber ops with other noncyber operations; ·· Perform effects assessment; ·· Establish persistent presence as required on threat networks.

From: [50].

• Geospatial area description describes the land-ocean configuration and constraints of the topography from ocean floor to littoral sea-land coastal areas and the terrain of land features; • Order of battle of the Red and Blue opposing forces, and the numbers of platforms and their locations and objectives; this drives the threats (to both forces) and naval objectives; • Scenario progression is the series of actions that both naval forces take over time and the events as they interact. This interaction includes conflicts in the physical-kinetic, electronic, cyber, and political-social domains. Background and Situation

This scenario takes place in the fictional Kandago Sea where two nations have contested claims over a series of 10 islands across a 400-km chain in the Kandigan Straits. The dispersed islands are contested based on complex historical claims and counterclaims due to the nations’ authority in their territorial seas (out to 12 nautical miles) and jurisdiction in the exclusive economic zones (out to 200 miles) that intersect at the minor islands. The islands had little value until recent decades, when their proximity to potential oil and mineral resources, and their strategic location in the path of major shipping lanes have increased the level of geopolitical competition. The islands provide strategic positions to monitor the sea lanes and enable limited naval presence to control or harass shipping. In addition, they can provide fueling, logistics, and command and control to enable future conflicts with the Green Island. The largest island has

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terrain suitable for a military air strip enabling a strategic placement for air surveillance and strike [52]. Green island nation has been the historical owner of the islands and has been concerned about losing sovereignty over the chain within its declared sovereign territorial waters. This scenario takes place over a 4-day period. Red nation political rhetoric and air reconnaissance over the first 2 days (days –2, –1) provide indications and warnings (I&W) of potential Red nation intent to annex one or more of the islands in the chain. This warning allows Blue forward presence naval forces to move toward the area and to use air capabilities to deploy unattended subsurface and seabed sensors. The Blue Surface Action Group (SAG) is conducting maritime security operations (MSO) and is immediately called to move toward the crisis [53]. The Blue force may be called upon to conduct littoral operations in a contested environment (LOCE) if nonkinetic operations cannot mitigate the aggression. Commanders’ intents, based on strategic orders are: • Red force commander’s intent: Swiftly position sufficient land forces and weapons to secure two strategic islands (Kandigan A and Kandigan B) before military, diplomatic, and regional pressures can dislodge the force and require a retreat to port. Defend the landing forces with SUW by denying defending forces access to the straits, and only resort to landbased strategic antiship missiles as a last recourse. • Blue force commander’s intent: Respond quickly to support treaty ally and economic partner Green nation in repelling Red nation aggression. Move to position and stall annexation landing operations by presenting a military threat sufficient to allow regional political powers to force Red nation to cease landings and return to base. Threaten long-range counter-naval operations and the ability to perform expeditionary advanced base operations (EABO) if necessary, to blunt any attempted annexation of the island chain [54]. (This intent requires immediate ISRT to identify and track the Red SAG to prepare for surface action and expeditionary landings if necessary.) Geospatial Area Description

The conflict arises in an approximately 200 × 150-km area of contested straits between the Red mainland nation and the Green island nation (Figure 2.4). Red naval forces emerge from their naval port to insert landing ships on two of the contested islands (A and B) that are lightly populated by Green fishing colonies. Blue forces moved from Blue water operations to enter the Kandigan Straits from the east. Red forces can move from their naval base, loiter and assemble into an action group, and then traverse the 150 km to the target islands within 4–6 hours. The Blue force, conducting MSO 100 km to the east will re-

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Figure 2.4  Geospatial area and forces laydown.



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quire several hours to arrive at the straits, and in-range to conduct organic ISRT and kinetic operations [55]. The Blue force of 10 vessels will remain distributed across the straits and back into the blue water to avoid the Red force advantages: numerical at-sea vessel superiority, seabed sensor networks to Red ashore C2 centers, likely ISR assets on the island chain, and land-based missile threats. Order of Battle

Red and Blue have SAGs (temporary or standing organization of combatant ships, other than carriers, tailored for a specific tactical mission) that are equipped as compared in Table 2.8. Scenario Progression

Over a 2-day period (days –2, –1) Red nation provides I&W of the Red nation intent to annex one or more of the islands in the chain. Political rhetoric ratcheted up the accusations of Green nation’s over-fishing the straits and unfounded territorial claims. This warning causes regional partnership nations to call on Blue nation to prepare to move Blue forward presence naval forces toward the straits. The implication for ISRT activities is that the Blue SAG and supporting Blue nation capabilities must rapidly move from peacetime intelligence preparation of the operating environment to surveillance-reconnaissance (Table 2.9). This rapid shift requires the immediate refocus of space and other sources to the Blue port, SAG platforms, and potential targets of the aggression. As the SAG moves to the area of operations, it deploys organic ISR capabilities to focus on the threatening SAG. Reconnaissance is performed by unmanned and manned

Table 2.8 Order of Battle Red Naval Forces (Aggressors) Blue Naval Forces (Defenders) Land Force: Land Force: None 3 batteries, medium range (400 km) tactical antiship missiles. ·· 5 DDG guided missile destroyers (1 additional ·· 3 DDG guided missile destroyers in SAG, 1 in reserve in port); Helo each; ·· 4 landing ships with 3 Helo each, and small ·· 2 expeditionary sea base ships with EABO vessels (2 in reserve in port); marine battalion, 4 Helo, and small vessels; ·· 2 FFG-guided missile frigate; ·· 2 FFG-guided missile frigate; ·· 3 aux support ships; ·· 2 auxiliary support ships; ·· 2 tactical attack submarines. ·· 1 tactical attack submarine. Air Capability: Air Capability: ·· 12 long-range patrol aircraft with antiship ·· 5 UAV reconnaissance drones (long range); missiles. ·· 12 UAV reconnaissance drones (short range).



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Table 2.9 Activities Across the Phases of ISRT Operations ISRT Phase Cyber Space Air Surface

Subsurface Seabed

JIPOE Situation Surveil Intelligence Awareness Recon Cyber monitoring and initiate target access Orchestrate for aggregate Focus coverage per maritime collection orchestration priorities on combatants Normal fleet coordination of manned and unmanned sensor assets; Adapt deployed sensors, relay comms, to predicted threats, threat axis.

Targeting Fire Control Coordinated cyber node attack Focus on Support target coordinated fires priorities Coordinated Coordinated airair-surface surface fires target tracking nomination Coordinate manned, unmanned, and relay comms ASW and SUW operations for sensing, maneuvering Monitor-report early precursor adversary activities

DAYS →

Focus seabed sensors; move deep UUVs to position HOURS →

Issue and relay commands to enable sensors and target mines, torpedoes MINUTES

air capabilities to deploy unattended subsurface and seabed sensors, reconnoiter the SAG platforms, and extend the SAG’s signals collection capability. The following paragraphs summarize the events of days 1 and 2 that bring the scenario to resolution. This is a contrived result to illustrate exceptional ISRT capabilities, but it is but one of many possible outcomes, many of which do not result in Blue force success. The fictitious scenario is compressed in time and orchestrated to illustrate the dynamic and interactive role of ISRT. Day 1. After 48 hours of building political rhetoric and air reconnaissance over islands A and B, Red initiates the aggression with combatant and support ships departing port and forming the SAG to head toward the target islands A and B. Guided missile destroyers (DDG) deploy to defensive positions to guard the landing force and prepare for surface action if (and when) Blue SAG responds. The actions of Red and Blue forces on day 1 are summarized in Table 2.10. Blue force initiates focused ISRT and plans to move quickly from intelligence preparation to surveillance-reconnaissance and then targeting. The ISRT actions performed by Blue include: 1. Exploit commercial and national space imagery to locate the Red SAG force; identify ships moved from port and hull numbers. Prepare for the challenge of tracking, losing, and re-acquiring the SAG as it moves across dense commercial ship lanes to its targets. Task commercial SAR satellites to collect on areas that traverse the shipping lanes.

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Naval ISR Fusion Principles, Operations, and Technologies Table 2.10 Day 1 Actions

Period Red Actions 0000–0600 Order to execute plan KANDIGAN 45; Move ships from port; assemble SAG to move. 0600–1200 Begin movement to target islands; Initiate EMCON (emission control); Disable AIS transponders. 1200–1800

1800–2400

Arrive and loiter 10 km off-islands: ·· Issue warnings via radio and social media. SAG ready to make land at 2400. Stand by to move to 3 km for landing ships; Insert special landing recon personnel; Await 1200 attack orders.

Blue ISRT Actions Receive ISR report—Red assembling SAG; Monitor ISR reporting, plan organic ISR upon arrival. Monitor cyber (social media) and EW channels; Heighten cyber alert status to prosecute and respond to potential Red cyber incidents or events; Launch organic UAV ISR to monitor Red SAG. Arrive in standoff position 40 km off islands; Monitor cyber and EW Channels, jam selected comm. channels, and coordinate with ashore cyber operators. Maneuver to demonstrate ready-for-surface engagement; Launch UAV aircraft to monitor Red landing activities with IR night vision full motion video sensors.

2. Analyze topographic maps, bathymetry of the Kandigan island chain to estimate likely target island(s) and landing sites. Direct available ISR collection to those areas to support any Blue marine expeditionary ground activities. Identify any potential for Red mining operations and requirements for countermining. 3. Deploy UUVs from ships, SONAR sensors (surface) from UAVs, and task submarines to predeployed seabed SONAR sensors to locate Red submarine threats and distinguish surface combatants from the dense commercial traffic. Track DDGs and plan counternaval operations to prepare to engage the surface force. Initiate persistent unmanned air vehicle (UAV) coverage of the most likely target islands and combatants. 4. Monitor civilian channels in social media (both in Red and Green) to aggregate popular support/opposition for Red naval activities. 5. Receive strategic guidance updates from Blue ashore national command authority as regional diplomatic interactions place pressure on Red political leadership. Based on the ISR results, Blue prepares to perform countertargeting (using deception and decoys) to reduce the ability of the Red SAG to target the Blue SAG as it moves within range of the land-based antis-ship missiles and Red DDG missiles. (Of course, Red wants to avoid a surface engagement with



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Blue due to political blowback; its objective is to quickly complete and secure its landing mission before Blue can disrupt the operation.) Day 2. Through the night of day 2 (Table 2.11), Blue forces maneuver to increase their ISR footprint and defensive posture, Blue submarine and UUVs are maneuvered to detect and hunt the Red submarine threat. Red forces initiate landing operations for A and B at 0530 with the objective of securing the two islands by 1500 to enable a Red press conference announcing annexation of “Restored National Lands to Protect Fishing” by 1600. At 0730, Red initiates a social media campaign propagating the meme that Green mainland fishermen have stepped up the harassment of the fishing villages of “Red National Lands on the Islands.” The message is picked up and propagated by Red regional news sources for morning news reports. By 1000, the situation escalates as Red landing forces are headed ashore, but are hampered by precision electronic warfare jamming of their communication network and cyber disruption of their afloat networks. Blue has maneuvered DDGs to coordinate unmanned air, surface, and subsurface vehicles while poising a potential threat to Red DDGs by long-range cooperative engagement if necessary. Blue increases cyber and EW activities to slow the C2 of the landing effort and feigning the landing of the EABO Marine battalion by helicopter and small vessels on island B to cause Red to consider implications of a regional conflict. By 1300, the landing is only Table 2.11 Day 2 Actions Period 0000–0600

0600–1200

1200–1800

1800–2400

Red Actions Initiate landing on schedule; Track Blue force maneuvering.

Blue ISRT Actions Maneuver DDGs to coordinate unmanned air, surface, and subsurface vehicles; Prepare for long-range cooperative engagement of Red DDGs if necessary; Increase cyber and EW denial, disruption activities. Provide C2 for landing, supply, and Feign the landing of the EABO Marine defensive support DDG vessels; battalion by helicopter and small vessels Incur stiff jamming environment and on island B; reduced comms. Receive approval to expand offensive cyber actions against Red port facilities and Red patrol aircraft. C2 unable to fully coordinate and monitor Reduce active engagement of cyber and forces; RF channels to monitor withdrawal. C2 detect imminent Blue marine force arrival; Withdraw forces. Continue disinformation campaign that Maintain unmanned vehicles on station Red forces protected island fishermen to provide ISR of Red forces returning from Green harassment, while Blue to port; forces carelessly aggravated the Monitor social media information situation by threatening Coast Guard campaigns and measure effectiveness. operations.

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partially completed and C2 is sufficiently confused that Red national command cannot determine the effectiveness of the operation; Red political leadership calls off the operation and issues a statement that “Red forces protected island fishermen from Green harassment, while Blue forces carelessly aggravated the situation by threatening Red Coast Guard operations.” Supporting social media campaign carries the meme with untrue anecdotal accounts of injured fishermen, Blue force landings on islands, and a Blue force missile launch against the Red fleet that failed in-flight. The ISRT actions performed by Blue include: 1. Receive strategic guidance updates from Blue national command authority as regional diplomatic interactions place increased pressure on Red political leadership using ISR-supplied intelligence to inform Red of regional knowledge of Red’s actions. 2. Locate subsurface threats; UUVs employed active SONAR to locate the Red submarine threat. They also pinged seabed sensors to “reporton-detection” via deployed once-shot float-to-surface relays that relay information via satellite communication. 3. Red fleet tracking: Blue space constellations (SAR imaging) had been tasked to monitor Red forces throughout the night and detected the landing assembly and preparations at 0500; these detections cued UAV ISR to track the landing activities. The constellations also track Red DDGs and provide updates to allow organic UAVs to track maneuvers and provide relay if cooperative surface-to-surface missile engagements are required. 4. Monitor and perform traffic analysis on Red frequency-hopping communication and C2 channels to support jamming operations; analyze the traffic patterns to distinguish operational phases. Blur mapped cyber network data to engage Red cyber networks that support the landing activities. 5. Continue to monitor civilian channels in social media (both in Red and Green) to measure the impact of Red information campaigns; conduct a counterinformation campaign to targeted channels to mitigate the effects of misinformation. Scenario Conclusion

This contrived scenario provides a basic insight into the large number of variables, the range of alternatives, and the complexity of choosing naval tactics and applying ISRT to achieve operational objectives. The role of ISRT is shown to be a critical component to enable maritime awareness, insightful choice of op-



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erational actions, and precision in conducting offensive operations from cyber to kinematics. Further scenarios are used throughout this book to illustrate ever more advanced ISRT concepts.

Endnotes [1] Feng, W., Y. Li, X. Yang, Z. Yan, and L. Chen, “Blockchain-Based Data Transmission Control for Tactical Data Link,” Digital Communications and Networks, 2020. [2] Eyer, K., and S. McJessy, “Operationalizing Distributed Maritime Operations,” March 5, 2019, https://cimsec.org/operationalizing-distributed-maritime-operations/. [3] Richardson, J. M., “A Design for Maintaining Maritime Superiority,” Naval War College Review, Vol. 69, 2016. [4] Popa, C. H., et al., “Distributed Maritime Operations and Unmanned Systems Tactical Employment,” Monterey, CA, 2018. [5] Mattis, J., “Summary of the 2018 National Defense Strategy of the United States of America,” 2018. [6] Larter, D. B., “Jim Mattis’ ‘Dynamic Force Employment’ Concept Just Got Real for the US Navy,” Defense News, July 16, 2018, https://www.defensenews.com/naval/2018/07/16/ jim-mattis-dynamic-force-employment-just-got-real-for-the-us-navy/. [7] Article 1(a) of the IMO Convention. For the IMO Focus on security, see https://www. imo.org/en/OurWork/Security/Pages/Default.aspx. [8] https://mc.nato.int/. [9] https://www.nato.int/cps/en/natohq/topics_88745.htm. [10] https://www.dst.defence.gov.au/. [11] “National Plan to Achieve Maritime Domain Awareness, The National Strategy for Maritime Security,” U.S. Coast Guard, 2005. [12] Nimmich, J. L., and D. A. Goward, “Maritime Domain Awareness: The Key to Maritime Security,” International Law Studies, Vol. 83, 2007. [13] Bueger, C., and T. Edmunds, “Beyond Seablindness: A New Agenda for Maritime Security Studies,” International Affairs, Vol. 93, No. 6, 2017. [14] U.S. Government, “Joint Publication 2-0 Joint Intelligence,” 2013, Figure I-3. The Intelligence Process, p. I-6. [15] Brown, J., “Strategy for Intelligence, Surveillance, and Reconnaissance,” Air University Press, 2014. [16] Smagh, N. S., “Intelligence, Surveillance, and Reconnaissance Design for Great Power Competition, Congressional Research Service,” Congressional Research Service, 2020. [17] Hoehn, J. R., and N. S. Smagh, “Intelligence, Surveillance, and Reconnaissance Design for Great Power Competition,” Congressional Research Service, June 4, 2020, p. 17, https://crsreports.congress.gov/.

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[18] “The U.S. Army in Multi-Domain Operations 2028,” 2018. [19] “DoD Data Strategy,” 2020. [20] “National Maritime Domain Awareness Plan for the National Strategy for Maritime Security,” U.S. White House Office, 2013. [21] Bommakanti, K., “India and China’s Space and Naval Capabilities: A Comparative Analysis,” ORF Occasional Paper, 2018. [22] Office of the Chief of Naval Operations, “The Navy Unmanned Undersea Vehicle (UUV) Master Plan,” Technical Report, ADA511748, 11-09-2004. [23] Button, R. W., et al., “A Survey of Missions for Unmanned Undersea Vehicles,” Santa Monica, CA: Rand National Defense Research Inst., 2009. [24] Chief of Naval Operations. U.S. Navy Program Guide 2017. [25] Holler, R. A., A. W. Horbach, and J. F. McEachern, “The Ears of Air ASW – A History of U.S. Navy Sonobuoys,” Warminster, PA: Navmar Applied Sciences Corporation, 2008, https://www.worldcat.org/title/ears-of-air-asw-a-history-of-us-navy-sonobuoys/ oclc/720627294. [26] DARPA Ocean of Things, https://oceanofthings.darpa.mil. [27] https://news.lockheedmartin.com/2006-01-18-Lockheed-Martin-Awarded-144-3Million-for-the-Navys-Advanced-Deployable-System. [28] Board, N. S., and N. R. Council, “C4ISR for Future Naval Strike Groups,” 2006. [29] Chief of Naval Operations. U.S. Navy Program Guide 2019. [30] Board, N. S., and N. R. Council, “C4ISR for Future Naval Strike Groups,” 2006. [31] https://defense.info/defense-systems/maritime-autonomous-systems-their-potentialcontribution-to-the-isr-mission-set/. [32] Best, Richard A., C. C. Bolkcom, and Foreign Affairs, Defense, and Trade Division. “Airborne Intelligence, Surveillance & Reconnaissance (ISR): The U-2 Aircraft and Global Hawk UAV Programs,” Congressional Research Service, Library of Congress, 2000. [33] https://www.navy.mil/Resources/Fact-Files/. [34] “Space Operations,” Joint Publication 3-14, 2018. [35] “Challenges to Security in Space,” Defense Intelligence Agency. [36] Spires, D. N., “Beyond Horizons: A Half Century of Air Force Space Leadership,” Colorado Springs: Air Force Space Command in Association with Air University Press, 1998, p. 7. [37] “Satellites,” Glactics, 1997, http://satellites.spacesim.org/english/main.html. [38] Skinner, B., “Ground-Based Weapons: Kinetic Antisatellite Weapons,” Space Security Index, 2020. [39] Lervolino, P., R. Guida, P. Lumsdon, J. Janoth, M. Clift, A. Minchella, and P. Bianco, “Ship Detection in SAR Imagery: A Comparison Study,” Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), July 23–28, 2017, pp. 2050–2053.



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[40] Van Etten, A., “Object Detection in Satellite Imagery, a Low Overhead Approach, Part I,” August 30, 2016, https://medium.com/the-downlinq/object-detection-in-satelliteimagery-a-low-overhead-approach-part-i-cbd96154a1b7. [41] Millhouse, P., “Next-Gen Persistent Maritime Security with Commercial Space Assets,” WEST 2020, March 2, 2020. [42] The AIS is a maritime ship identification and location system that was mandated in December 2004 by the IMO. All commercial vessels that travel in international waters are required to transmit ship identification and location (derived from GPS) that can be received by AIS receivers ashore, on other ships, or from satellites. AIS transponders broadcast messages at regular intervals over two designated VHF channels. [43] HawkEye typical geolocation performance for a trio of satellites varies between 0.1 to 0.2 km depending on satellite-to-target geometry at the time of measurement. The geolocation is provided by: (1) Measuring and comparing time-of-arrival (TOA) and frequency-ofarrival (FOA) of a signal between 2 or all 3 satellite receivers, (2) using GPS to maintain precise estimates for the position and velocity of the spacecraft and their receive, and (3) performing multilateration to estimate the signal emitter location. See Sarda, K., N. Roth, R. E. Zee, Dan CaJacob, Nathan G. Orr, “Making the Invisible Visible: Precision RFEmitter Geolocation from Space by the HawkEye 360 Pathfinder Mission,” Proc. 32nd Annual AIAA/USU Conference on Small Satellites, Paper SSC18-II-06, 2018. [44] https://www.asc-csa.gc.ca/eng/satellites/radarsat/technical-features/radarsat-comparison. asp. [45] https://www.iceye.com/hubfs/Downloadables/ICEYE_SAR_Product_Guide_2021_ V4.0.pdf. [46] https://www.newspace.im/constellations/planet-skybox. [47] https://www.satimagingcorp.com/satellite-sensors/other-satellite-sensors/dove-3m/. [48] “The National Military Strategy for Cyberspace Operations,” U.S. Government, 2006. [49] Thiele, R. D., “Game Changer – Cyber Security in the Naval Domain,” ISPSW Strategy Series: Focus on Defense and International Security, No. 530, 2018. [50] Based on Joint Chiefs of Staff, Joint Terminology for Cyberspace Operations Memorandum, Attachment 1 Cyberspace Operations Lexicon, November 2010. [51] Joint Publication 3-32 Joint Maritime Operations, June 8, 2018, validated on December 16, 2020. [52] In naval tactics, a strategically placed island can be considered to be an unsinkable carrier that provides maritime power projection from a hardened location. See Hughes, W. P., Jr., and R. P. Girrier, Fleet Tactics and Naval Operations, Third Edition, U.S. Naval Institute Press, 2018. [53] MSO are operations to protect maritime sovereignty and resources and to counter maritime-related terrorism, weapons proliferation, transnational crime, piracy, environmental destruction, and illegal seaborne migration. [54] “Expeditionary Advanced Base Operations (EABO) Handbook Considerations for Force Development and Employment,” Marine Corps Warfighting Lab, Concepts & Plans Division, June 1, 2018.

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[55] Cruise ships can traverse 10 km (at about 30 knots) in 10 minutes; it is reasonable for the Blue fleet to traverse 100 km within sever­al hours to arrive on station. ­

3 Distributed Maritime Operations The concept of distributing naval forces is relatively new because the fleet has traditionally spaced surface assets to provide massed defense while enabling precisely coordinated attacks. The entire subject of naval tactics (the techniques of action) is focused on protecting the fleet while enabling it to effect decisive strikes. Esteemed naval tacticians, Captain Wayne Hughes (USN Ret.) and coauthor Rear Admiral (RADM) Robert Girrier (USN Ret.) have cautioned: “Today, missiles of various ranges and homing characteristics can be placed in manned and unmanned ships and aircraft at a relatively low cost. With the onset of information warfare concepts, this looks like the beginning of a lasting change in naval warfare” [1]. Indeed, DMO introduces the wide distribution of forces, the arming of every vessel, the introduction of weapons on unmanned vessels (surface and subsurface) and aircraft, and a lasting change in naval warfare. With new naval concepts come new tactics, technologies, and operations that offer opportunities and risks that must be addressed. DMO introduces a drastic shift in operations and tactics, and an increased dependence on technologies: sensors, networks, computation, and decision support. In this chapter, we introduce the tactical considerations for distributing operations and the implications for C2ISR architecture and battle management. In subsequent chapters we detail the technology considerations to provide the ISR capabilities necessary for DMO.

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3.1  Introduction to DMO Since the Battle of Midway demonstrated the potential for airpower to dominate naval engagements, the U.S. Navy structure has revolved around the carrier battle group. The potential for force projection by the carrier’s strike aircraft constituted a dominant threat for decades when the United States control of the seas and of the air was uncontested. For most of the 1990s and the early 2000s the USN force structure was based around the core of 12 carrier battle groups [2]. During the same timeframe, U.S. naval strategy included having the capability to field two amphibious warfare brigades standing ready to project power ashore. This amphibious capability required 38 amphibious ships, which is a number that was rarely met in practice. Most often, amphibious power was deployed as marine expeditionary units (MEU) embarked on three large vessels (landing helicopter dock (LHD) or landing helicopter assault (LHA), landing platform dock (LPD), and landing ship dock (LSD)). However, this number is being substantially reduced to 24 to 28 large amphibious ships in the FY2022 shipbuilding plan [3]. The reduction in large combatant vessels is, in part, due to recognition that in recent years the threats posed by land-based, antiship missiles (ASM) and enemy submarines have increasingly put such high value assets at risk. As a result, a significant portion of the battle group’s offensive potential is spent protecting the carrier rather than being available for strike operations [4]. Indeed, as early as 1990, the Cato Institute called into question the level of air and surface resources required to ensure a carrier’s survivability [5]. More recently, an Air Force study challenged the notion that the carrier-based airwings could supply the overwhelming sustained strike capability that has often been touted [6]. Concentrating combat power in a relatively small area has two distinct disadvantages—simplifying the enemy’s targeting problem and limiting the area over which our naval forces can project offensive capability. The CNO issued a challenge in January 2016 to consider alternative fleet structures [7]. The concept that has now gained the most traction is DMO, which is developing as a means to address these two issues. The Navy’s current 30-year shipbuilding plan represents a “once in a generation change” that pivots to support the DMO concept [8]. By 2018, the CNO’s design for maintaining maritime superiority 2.0 fully embraced DMO and directed continued development of the concept [9]. DMO disperses combat power in ways that complicate the adversary’s ability to engage friendly forces. Deploying in a larger number of smaller groups of combatants and adding unmanned surface and subsurface vehicles as decoys causes the enemy to dilute surveillance—a tactic known as countertargeting. DMO goes beyond merely dispersing combatant vessels and integrates expeditionary littoral bases from which land-based antiship and antiair missiles become part of the network performing antiaccess and area denial (A2AD)



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functions. A2AD is a counterengagement strategy that relies on the ability to threaten the enemy’s freedom of movement and limits their ability to concentrate force, making it difficult for the enemy to effectively mass combat power sufficient to threaten our forces [10]. The offensive strategy of DMO has its roots in the concepts of distributed lethality, which was widely discussed in the CNO’s guidance [9]. Rather than concentrating combat power in a few highly capable ships, distributing lethality aims to go beyond the defensive considerations outline, and provide the greater area covered by networked offensive weapons to hold enemy forces at risk. Expeditionary bases can also be used to support short-term logistical needs of surface, air, and subsurface assets to extend the range and duration of an operation. The goal is not to control the seas everywhere at all times, but to have the agility to hold any strategic maritime region at risk at any time. The force structure and command and control systems needed to implement DMO is somewhat different than that which the Navy has maintained for the last several decades. In particular, the distributed lethality necessary for effective DMO would imply a force structure that includes enhancing the lethality of individual minor combatants by increasing the ability of all combatants to share intelligence and targeting information. All vessels can participate in lethal action, even if organic sensors cannot reach targets; the fleet will network and coordinate their sensors and weapons: Enhanced surface ship lethality would allow smaller ships such as cruisers, destroyers, and littoral combat ships to operate in hunter/killer SAGs that could function autonomously, screen larger formations, and/or hold land targets and sea lanes of communication/commerce at risk. To succeed, it is imperative to distribute anti-air, ASW, and ant-surface fires. Future weapons such as long range ASW weapons, rail guns, and new classes of missiles may be needed to realize this level of lethality [11].

Networking for integrated fires requires persistent, organic over-thehorizon (OTH), and thus overhead, surveillance. Broader deployment of the Fire Scout unmanned aircraft system (UAS) is a first step, but integration with airborne and particularly satellite surveillance will be critical to the success of DMO. In order to process and disseminate intelligence and targeting information to a dispersed network of vessels, a more robust and distributed command and control paradigm must be adopted. Particular consideration needs to be given to maintaining useability of the electromagnetic spectrum and to ensuring secure cyber and communication protocols. While this book does not provide details on methods to network assurance and cyber security, these are implied capabilities that are covered in depth in texts dedicated to those subjects. In the remainder of this chapter, we explore considerations for DMO tactics and battle management in the DMO environment.

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3.2  DMO Tactical Considerations In his recent planning document, the CNO focuses attention on the necessity of DMO by stating that “Emerging technologies have expanded the modern fight at sea into all domains and made contested spaces more lethal. Ubiquitous and persistent sensors, advanced battle networks, and weapons of increasing range and speed have driven us to a more dispersed type of fight. Advances in artificial intelligence and machine learning (AI/ML) have increased the importance of achieving decision superiority in combat” [12]. With such a force, where “everything is a shooter and a sensor” (or, as RADM Fanta says, “if it floats, it fights” [13]) how does this impact the tactics of naval forces? The force that is seamlessly interconnected by grids of networks and is orchestrated with AI/ML–based decision support must impact fleet tactics. Specifically, how does DMO influence tactics, or as Hughes contends, “… the handling of forces in battle… where maritime issues are at stake” [14]? DMO is defined by Braithwaite as “…an operations concept that leverages the principles of distribution, integration, and maneuver to mass overwhelming combat power and effects at the time and place of our choosing…” [15]. In the next paragraphs, we consider the implications of distribution, integration, and maneuver, on naval tactics. Distribution. DMO envisions distributed platforms, weapons systems, and sensors across all domains. In contrast to the carrier battle group concept, DMO seeks to “…‘distribute offensive capability geographically’: This speaks to a wider dispersion of ships, in order to hold an enemy at risk from multiple attack axes, and force that enemy to defend an increased number of vulnerabilities, created by that dispersion. This point suggests what will become clear later, and that is the disaggregation of forces, which is part and parcel of DMO.” Thus, “a general paucity of assets in any high-end fight, in any theater can only be addressed by the precise delivering of only the exact right force to the exact right place at the exact right time” [16]. An example of the benefits of such a dispersion of forces is suggested by Jensen: Distributed maritime operations take a different point of departure. Rather than attack mainland command and intelligence assets in a crisis with China (e.g., AirSea Battle) or threaten commercial shipping lanes (e.g., offshore control), small, dispersed land and sea detachments threaten the ability of Chinese forces to concentrate from within their anti-access/ area denial umbrella. These forces deny Chinese freedom of movement along key sea and air lines communication. Distributed forces change the adversary’s cost calculus and buy time for flexible deterrence options and assembling a joint task force [17].



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Integration. The confluence of vision and technological innovation have driven the evolution of naval forces from collections of independent platforms, weapons systems, and sensors to synergistic, integrated, systems-of-systems sharing real-time information, affording every netted platform a common understanding of the battlespace with the same fire-control quality information. Commanders and autonomous platforms share a common operational picture and share a common fire-control solution to engage an adversary. Rear Admiral Rodney Rempt projected this evolution to an even more sophisticated future: A future in which the Navy’s tactical grid would one day be understood as, simply put, an agnostic network of weapons and sensors, controllable by any number of nodes, and without regard to where those weapons or sensors or controlling nodes might be deployed or even in which unit they existed. In the future, if an inbound threat were to be detected, this agnostic, dispersed grid would determine which sensor(s) would be most appropriate, and then, when necessary, the system would pair the most capable and best located weapon with that sensor(s) in order to efficiently engage the threat [16].

This sophisticated future envisioned by Rempt is a necessary tactical advantage for a victorious naval force. Rapid, decisive action, with an informed understanding of the battlespace leads to maritime superiority. Maneuver. Maneuver suggests the employment of rapid, unexpected, deterrent actions upon an adversary, allowing the exploitation of uncertainty and achieving surprise. As a result, the adversary “…perceives he has lost control and becomes the victim of disruption, confusion, and disorganization… The adversary finds this rapid chain of unexpected events impossible to cope with effectively” [18]. Distributed, highly integrated, maneuverable DMO-enabled forces, comprised of crewed and uncrewed vessels, are optimized for adaptability, which leads to tactical advantage. Some examples of defensive and offensive engagements yield further insight. Distributed defensive strategies consider two distinct alternatives to a multiaxis, multimissile engagement [19]: • Clustering ships tightly to concentrate and distribute defensive capabilities to avoid any one ship being overwhelmed; • Dispersing ships widely (DMO) while sharing adversary warning and targeting data to enable the distributed assets to respond cooperatively in defense, as a single fighting unit. In considering offensive action or coordinated attack:

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…The offence might use a mix of crewed and uncrewed vessels. One option is to use three ship types: a large, well-defended crewed ship that carries considerable numbers of various types of long-range missiles but which remains remote to the high-threat areas; a smaller crewed warship pushed forward into the area where adversary ships are believed to be both for reconnaissance and to provide targeting for the larger ship’s long-range missiles; and an uncrewed stealthy ship operating still further forward in the highest risk area primarily collecting crucial time-sensitive intelligence and passing this back through the smaller crewed warship onto the larger ship in the rear… An alternative concept is to have a large crewed vessel at the center of a networked constellation of small and medium-sized uncrewed air, surface and subsurface systems. A large ship offers potential advantages in being able to incorporate advanced power generation to support emerging defensive systems like high energy lasers or rail guns. In this, the large crewed ship would need good survivability features, suitable defensive systems, an excellent command and control system to operate its multitude of diverse uncrewed systems and a high bandwidth communication system linking back to shore-based facilities and data storage services [19].

The integration of distributed platforms, weapons systems, and sensors has profound implications on tactical advantage. This advantage is manifested in the synergies afforded by distribution, integration, and maneuver. Together these attributes of DMO enable enhanced maritime situational awareness, realtime sharing of a common operational picture and fire-control quality information across all platforms, and ultimately a decision advantage that enables action well before an adversary can react. These factors—distribution, integration, and maneuver—are literally game changers, as noted by Williamson, “An observable ocean will significantly change naval operations, and today’s game of Battleship will give way tomorrow to chess when every player can see the gameboard clearly. In that case, the advantage will go to the strategist who can best foresee future moves, understand environmental factors, and take the most rapid, decisive actions to achieve the advantageous position most consistently” [20].

3.3  DMO Architecture and Elements In formulating an architecture capable of achieving the connectivity needed for distributed networked fires in real time, it is useful to consider a tactical grid of connected, distributed nodes. The nodes can be sensors, weapons, communications, and decision nodes. Historically we have seen this concept mature through several phases. As missile technology began to surpass direct fire guns in the 1950s it became necessary to share targeting information between missile-capable vessels. The result was the tactical data line (TDL, or TADIL) sys-



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tems. The next major evolution was the Aegis system, which matured through the 1970s and allowed coordination of all of an Aegis-capable battle groups weapon systems. Aegis originally was envisioned as a defensive system coordinating disparate weapons systems such as the Phalanx® close in weapon system (CIWS)—a 20-mm gatling gun for short-range missile defense—and the Mk41 vertical launch system (VLS) capable of deploying a variety of missiles for self-defense and strike. The primary sensor for Aegis is the AN/SPY-1 radar which is capable of search, track, and midcourse missile guidance functions but other short-range sensors and terminal guidance radars are integrated as well. The latest evolution, made publicly known in the early 1990s, is the CEC, which allows for sensors to share raw data, as opposed to high-level symbolic information about the situation. This allows for a combatant vessel to view another vessel’s radar as its own and develop actual targeting solutions for its weapon system based on the other vessel’s sensor feed [16]. DMO would take CEC a step further and would leverage AI to implement decision nodes that are tightly coupled and support high-level human decision making in the presence of the chaos of battle. The AI could provide decision support logic to determine which vessels were in the best position to fire upon an enemy, what weapons they should employ, and which sensors should support the engagement. At the same time, defensive decisions will also be made, such as which vessels are at risk of attack and what countermeasures they should employ. Georeferenced data, automated situation recognition, machine learned detection of activities, knowledge of observed and models of anticipated adversary TTPs, may all be employed to recognize threatening behaviors [21]. Using this prior knowledge and applying multisensor fusion analysis can all be processed within the DMO context to provide decision support by AI at speeds far greater than humans alone could accomplish. The resulting decision superiority (timely and effective decision making) is a critical combat multiplier in the DMO paradigm. The various nodes to be integrated into the tactical grid include not only the traditional set of sensors and weapons that already exist in the fleet, but also the emerging technologies, platforms, and weapon systems that will enable DMO [22]. This could include USVs of various types, such as large USVs that serve as weapons platforms, perhaps deploying the proposed long-range antiship missile (LRASM), and medium-sized USVs that would function primarily as sensor platforms. Unmanned air platforms will also be key nodes in DMO. The MQ-8B Firehawk can be deployed from a variety of ships since it requires only a small helipad to launch. Having trained operators and maintenance personnel is still necessary, however. The MQ-8B Firehawk can serve as both a sensor and a weapons system. Firehawk is equipped with an RDR-1700B X-band radar capable of all-weather search and targeting and can share that targeting information

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with other platforms via CEC. Should immediate engagement be required, Firehawk is equipped with the advanced precision kill system (APKS)—laser guided 2.75-in rockets [23]. The Scan Eagle UAS provides a different kind of flexibility with pneumatic launch and skyhook recovery and provides persistent day/night and thermal imagery for over 20 hours on station [24]. In order to ensure reliable, high bandwidth communications OTH, it will be necessary for the DMO architecture to incorporate communications relay nodes. There are two basic options for such nodes: satellites or UAS. While geostationary communication relay satellites have been used in the past, the need for high bandwidth and low latency are driving the solutions to LEO satellites [25]. The commercial world is already addressing high bandwidth satellite infrastructure with products like the Starlink satellite internet constellation, offering 60 to 90 Mb/s rates or higher for residential customers. Whether the military will procure bandwidth from such commercial providers or invest in their own technology has been hotly debated over the last few years [26]. The trade-offs are lower cost for commercial systems versus direct control, secure protocols, and availability of military systems. Some vendors are marketing directly to DoD as a result. A notable example is the Viasat XVI, a Link-16 enabled satellite communication relay [27]. There are many UAS options which may offer low-cost alternatives [28], ranging from dedicated Global Hawk relay platforms to the small, unmanned multirotor aerial relay [29].

3.4  All-Domain C2 Battle Management At present, overall maritime mission command and control falls under the Joint Maritime Combatant commander. Responsibility is then further distributed to separate commanders: first to regional task forces (as the force is usually distributed geographically), and then by mission and domain. Each task force will have a commander for each of the following roles for which their task force has capability and responsibility: • Surface warfare; • Air and missile defense; • Antisubmarine warfare; • Mine warfare; • Strike warfare; • Amphibious operations. There are a host of other specialized minor operations requiring delineation of C2 requirements as well, ranging from counterdrug and maritime



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interdiction to noncombatant evacuation, humanitarian support, and logistical missions such as maritime prepositioning [30]. The location of the C2 centers for these operations has traditionally been selected based on available communications, cryptographic, and sensor resources, as well as proximity to the operational assets involved and the geographical considerations of the mission itself. A major limitation on DMO is that although one can assert that “if it floats, it fights” [31], that different ship types are necessarily optimized for certain roles and staffed with sailors and commanders with expertise in those roles. Designating a mine-sweeping ship as the antiair mission commander would never be ideal, for instance. In such a case, none of the required sensors, weapons, or human capital would be resident on the minesweeper, and would all need to be imported from other nodes, imposing latency and reliability concerns. It is doubtful that DMO can be extended so far as to view specialized ships as homogeneous assets. Nonetheless, the ability to share the totality of the operational picture among several ships provides greater resilience by enabling the seamless transfer of operational command from one ship to another, or even to shore. A useful principle for deciding where operational command should physically reside is to consider the observations of former Pacific Fleet commander, Admiral Scott Swift. Swift has pointed out that a century ago, naval officers understood that C2 was about leadership and tactical expertise. Over the course of the late twentieth century, communications and computers have been included and the term C4 has replaced C2. Swift thinks this is a mistake because it conflates the technical means of achieving control with the cognitive processes of making command decisions [32]. Regardless of whether electronic communication and decision support tools are available, a ship’s captain must still command his ship. For this reason, Admiral Swift published a set of fighting orders in part to serve as guidance to commanders who have been cut off from their higher C2 elements during distributed operations [16]. DMO operations are, of course, dependent on reliable and secure networking to assure the benefits of distributed and highly automated decision making, but commanders must be prepared to degrade gracefully (or abruptly) and sustain operations. Distributing classified information will be required to achieve information sharing at the level required for DMO. In addition to requiring appropriate cryptographic assets and classified information systems on each ship, the effort to secure classified data in motion will increase substantially. More classified data will be exchanged over radio frequencies, meaning more transmissions over longer ranges, which increases exposure to interception. Sharing the common operational picture is currently done in the U.S. Navy to provide high-level situational awareness. However, to enable DMO requires sharing actionable tactical information which would support targeting decisions, weapons selection, and more. This capability has been described

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as a battle force manager (BFM) [16]. The entire BFM functionality would need to be replicated on each ship that would be capable of assuming the maritime operational commander (MOC) role. BFM capabilities would include at a minimum: • Maintaining connectivity to all nodes; • Monitoring the status of the ordinance on every node; • Health and status of each node, such as damage and fuel; • Understand operational capabilities and limitations of each node; • Ability to change operational parameters in any sensor or weapon system; • Explainable AI-based decision support that describes distributed, automated decision recommendations, rationale to allow rapid concurrence, adjustment, or rejection by decisionmakers in the CIC; • Explainable sensor/weapon/target pairing algorithms and recommendations; • Data storage, sorting, analysis, and presentation.

3.5  A DMO Scenario Building on the scenario introduced in Chapter 2 we provide a particularly illuminating vignette based on a published challenge problem by Eyer and McJessy [16] to portray the disruptive benefits of DMO. This scenario details the integrated response of a DMO-enabled naval system of systems as it confronts a sophisticated nation-state threat with hypersonic weapons, and one can postulate that the DMO-enabled force includes assets from seabed to space. This scenario (Figure 3.1) takes place in the Kandago Sea where two nations have contested claims over a series of ten islands across a 400-km chain in the Kandigan Straits; as in Chapter 2, the Red naval forces are beginning an assault on the contested island chain and distributed Blue forces, 350-km out to sea, are moving to defend the islands in support of Green nation. 1. In this case, Red nation will attack the approaching Blue naval fleet with hypersonic glide missiles from two ground batteries located on the coast. The flight time of these Mach 7 missiles is less than 5 minutes from coastal launch to the Blue fleet 350 km away. 2. In this agnostic grid, the launch is detected by multiple, mutually reinforcing methods, including (1) a constellation of IR sensing satellites and (2) a constellation of relay satellites that relay the IR signature

Distributed Maritime Operations

Figure 3.1  DMO configuration in the Kandigan Straits.

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of missiles to the fleet and to other satellites to enable rapid missile acquisition, handoff between satellites, and enable the fleet to establish a track. The sensing satellites coordinate a combination of wide and medium field-of-view sensors to detect, track, and then handoff track custody to close-in defensive weapon systems in the Blue fleet [33]. 3. The Blue fleet shipboard phased array radars are automatically changed to maximize its tracking capability. As more sensors are automatically brought to bear, a precise track, including origin and aim point, is generated. 4. At the same time, decisions are made at the strategic and operational levels, decisions dramatically aided by the application of artificial intelligence: Is the threat real? What asset(s) are under threat? What hard- and soft-kill techniques and systems are best employed? What systems are both in position and possess the capability and capacity necessary for engagement? What is the optimal engagement timeline? What additional sensors should be brought to bear, and when? Jamming? Chaff? Decoys? From whom and when? Who shoots? When do they shoot? What ordinance do they shoot? How many rounds? 5. Orders are automatically issued to concerned units, yet the entire network, including other decision nodes, remains fully cognizant of the larger picture. The battle management system has built-in redundancies so that if one node is destroyed, another automatically and seamlessly steps in. All of these decisions can be automated, if desired, in order to maximize speed and the optimal response, provided that commanders allow for that automation. Ultimately, only the necessary and best systems are matched to the threat, at only the right time, maximizing effect and minimizing the waste of limited resources. The most effective and efficient method of engagement becomes routine. Note the diversity of sensors implied, in type and capability, their dispersion, their seamless integration, and the orchestration provided by AI-enabled decision support agents that can reason on timelines appropriate for the find, fix, and finish process necessary to defeat a sophisticated threat. Also of note, is the resilience that is offered by DMO-enabled forces, in particular their seamless integration. In the event one node is destroyed, other nodes with an identical operational picture and firing solution readily step in.



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Endnotes [1] Hughes, W. and R. Girrier, Fleet Tactics and Naval Operations, Annapolis, MD: Naval Institute Press, 2018, p. 132. [2] “U.S. Ship Force Levels,” Naval History and Heritage Command, https://www.history. navy.mil/research/histories/ship-histories/us-ship-force-levels.html [3] Cancian, M., “Stormy Waters Ahead for Amphibious Shipbuilding Plan,” July 1, 2021, https:// breakingdefense.com/2021/07/stormy-waters-ahead-for-amphibious-shipbuilding-plan/. [4] Torvold, W. D., Defending the Aircraft Carrier: Doctrine and Technology for Survival. Newport, RI: Naval War College, 2000. [5] Isenberg, D., “Cato Institute Policy Analysis No. 134: The Illusion of Power: Aircraft Carriers and US Military Strategy,” 1990. [6] Grant, R., “The Carrier Myth,” Air Force Magazine, Vol. 82, Issue 3, 1999. [7] Richardson, J. M., “A Design for Maintaining Maritime Superiority. Version 1.0.,” Chief of Naval Operations Washington United States, 2016. [8] O’Rourke, R., “Navy Force Structure and Shipbuilding Plans: Issues for Congress,” Congressional Research Service Report 19, 2018. [9] Richardson, J. M., “A Design for Maintaining Maritime Superiority,” Washington D.C.: U.S. Navy, 2018. [10] Jensen, B., “Distributed Maritime Operations: Back to the Future?” April 9, 2015, https:// warontherocks.com/2015/04/distributed-maritime-operations-an-emerging-paradigm/. [11] Rowden, T., P. Gumataotao, and P. Fanta, “Distributed Lethality,” Proceedings Magazine Vol. 141, No. 1, 2015, 1343. [12] Gilday, M., CNO NAVPLAN, Washington, D.C.: United States Navy, 2021. [13] Freedberg, S. J., “‘If It Floats, It Fights’: Navy Seeks ‘Distributed Lethality’,” Retrieved from Breaking Defense, January 14, 2015: https://breakingdefense.com/2015/01/if-it-floats-itfights-navy-seeks-distributed-lethality/. [14] Hughes, W. P., Fleet Tactics and Coastal Combat, Annapolis, MD: Naval Institute Press, 1999. [15] Braithwaite, K. J., “Advantage at Sea Prevailing with Integrated All-Domain Naval Power,” Washington D.C.: Department of the Navy, 2020. [16] Eyer, K., and S. McJessy, “Operationalizing Distributed Maritime Operations,” Center for International Maritime Security, 2019, http://cimsec.org/operationalizing-distributedmaritime-operations/39831. [17] Jensen, B., “Distributed Maritime Operations: Back to the Future?,” War on the Rocks, 2015, https://warontherocks.com/2015/04/distributed-maritime-operations-anemerging-paradigm/. [18] Wilson, G. I., “The ‘Manuever Warfare’ Concept,” Gazette Magazine, Marine Corps Association, July 17, 2019, https://mca-marines.org/blog/gazette/the-maneuver-warfareconcept/.

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[19] Layton, P., “Fighting Artificial Intelligence Battles: Operational Concepts for Future AIEnabled Wars,” 2021. [20] Williamson, W., “From Battleship to Chess,” U.S. Naval Institute Proceedings, July 2020, Vol. 146, No. 7, https://www.usni.org/magazines/proceedings/2020/july/battleship-chess. [21] TTPs refer to three categories of adversary behaviors:

Tactics: The employment and ordered arrangement of forces in relation to each other; Techniques: Nonprescriptive ways or methods used to perform missions, functions, or tasks; Procedures: Standard, detailed steps that prescribe how to perform specific tasks.

[22] 2019 Surface Navy Symposium in Washington D.C. [23] “Fire Scout Unmanned Aircraft System,” https://www.northropgrumman.com/what-wedo/air/fire-scout/. [24] ScanEagle, https://www.boeing.com/defense/autonomous-systems/scaneagle/index.page. [25] Erwin, S., “Space Force Finalizing Plan to Procure Broadband from Low-Orbit Satellites,” March 27, 2021, https://spacenews.com/space-force-finalizing-plan-toprocure-broadband-from-low-orbit-satellites/. [26] Hitchens, T., “Griffin: DoD Can’t Rely on Commercial Satellite Communications,” December 3, 2019, https://breakingdefense.com/2019/12/griffin-dod-cant-rely-oncommercial-satellite-communications/. [27] Miller, A., “Powered by Satellite, Link 16 Military Radio Set to Get a Huge Boost,” June 27, 2019, https://www.viasat.com/about/newsroom/blog/xvi/. [28] Carr, D. F., “Communications Relay Grows with Expansion of UAV Missions,” August 3, 2009, https://defensesystems.com/articles/2009/07/29/c4isr-1-uav-relay.aspx. [29] “Unmanned Multirotor Aerial Relay (UMAR),” https://www.dragonflypictures.com/ products/umar-tethered-uas/. [30] Joint Publication 3-32, Joint Maritime Operations, June 8, 2018. [31] Rowden, T. S., “Surface Force Strategy: Return to Sea Control,” San Diego, CA: Naval Surface Force Pacific Fleet, 2016. [32] Swift, S., “Master the Art of Command and Control,” Proceedings of the U.S. Naval Institute, Vol. 144/2/1,380, February 2018, https://www.usni.org/magazines/proceedings/2018/ february/master-art-command-and-control. [33] This is based on the U.S. Space Development Agency constellation concept. See Erwin, S., “The Pentagon’s Hyperfocus on Hypersonic Missile Threat,” Space News, August 25, 2012, https://spacenews.com/the-pentagons-hyperfocus-on-hypersonicmissile-threat/.

4 Naval Information Fusion Systems Navies have traditionally partitioned sensor systems into stovepipes in the mission areas of air warfare, surface warfare, antisubmarine warfare, electronic warfare, and missile defense. Despite the criticisms of stovepipes, this approach simplified acquisition of independent systems and enabled development and procurement to be focused on mission effectiveness in each area. TDLs interconnected these systems to the Combat Information Center (CIC), and the CIC to shooters (weapon systems) to engage targets. Integration of information from each mission area occurred at the centralized CIC and a common operating picture could be distributed to all participants that had access via TDLs or broader satellite communications. This portioning made sense and has been efficient to perform any automated fusion of sensor data just within, but not across, these mission domains. These functions merged their respective information about threats (objects, tracks, events, and activities) in the CIC where the commander and staff applied the current context to understand the situation presented to them. The desire to perform all-domain sensing brings the need to expand the domain coverage, increase the speed of sensing and fusion, and cause ISR to deliver targeting-quality information is a new factor driving significantly expanded automation. We now use the term ISRT to refer to this close integration of ISR to C2 and targeting. The desire for increased networking, NCW, and joint all-domain ISR-C2, requires even greater integration of systems. In this chapter, we examine a baseline of the current categories of naval sensing and fusion systems and information integration to perform joint ISR and C2, using, as an example, general U.S. naval systems that are representative of advanced international systems. We also introduce the computing and networking concepts and technologies that are enablers to extend the integration 69

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to a greater degree—to integrate organic and nonorganic sensors and sources, the subsurface and the seabed. While this chapter introduces the baseline state of the art, we will then provide greater depth in Chapters 6 (the data fusion process) and 7 (the network and distributed data fusion). The application of these technologies for naval ISR is being developed by international navies as they seek to apply AI capabilities [1].

4.1  Enterprise-Level Fusion The naval information enterprise is expansive and includes many diverse elements enumerated in Table 4.1, both afloat and ashore. It is a component of a larger information enterprise that encompasses all other service elements (Army, Air Force, Marines) that participate in all-domain operations, as well as the forces of partner nations that operate in a coalition. While many functions exist across these elements (e.g., cyber warfare, C2, EW, battle management, oceanography, and meteorology), we focus on just the many functional elements that comprise the ISR portions of the enterprise (Table 4.2). The integration of information across all these elements to perform distributed operations requires unprecedented coordination of standards, data representations, interoperable data links, distributed computing resources, sharing mechanisms and operating concepts, all at a fine level of granularity and at a massive enterprise scale. At this highest level of abstraction, the enterprise requires a common formal understanding of fundamental terms, formats, and processes, for example: • Enterprise governance (information technology (IT) strategy, resource management, risk management, performance measurement, optimization); Table 4.1 Elements of the Naval Information Enterprise Afloat Ashore ·· Task force command; ·· National command centers; ·· Strike group command(s); ·· Intelligence centers (national, services); ·· Organic sensors (seabed-to-air); ·· Naval fleet commands; ·· Organic TDLs (seabed-to-air); ·· Space sensing systems; ·· Space communication links. ·· Space communication, navigation centers; ·· Meteorological and oceanographic systems (METOC) centers.



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Table 4.2 Major Categories of ISR and Information Operations (IO) Sensing, Processing, and Exploitation Subsystems ISR Areas ISR Enterprise

ISR Subsystems Capabilities Common computing environment with service-oriented architecture to enable ISR and C2 services to share information, maintain a common understanding of the battlespace, and coordinate sensing across all services.

Maritime domain awareness (combat capability all source data) Maritime domain awareness (unclassified for commercial vessels) Air surveillance and air defense

ISR aggregating, correlating, and fusing all source intelligence in real-time and near real-time processing analysis for tracking, targeting, and exploitation. Coordination of both afloat and ashore elements.

Missile defense

Signals intelligence/ electronic warfare/ information operations Antisubmarine warfare (ASW) and seabed warfare Cyber and network defense

Comparable USN Systems Consolidated afloat networks and enterprise devices (CANES), integrated shipboard network system (ISNS) Defense common, ground system DCGS-N

Visualize vessel positions and tracks on a map (terrestrial and satellite AIS, coastal radar, and satellite imagery based positions in time (POSITS)). Complex searches, alerting, and vessel risk scoring. Multiple source correlation. Detect and visualize air tracks (commercial, combat) from all sources (radar, IFF, and AIS) and identify tracks; designate for challenges and engagement. Detect and track surface vessels and identify tracks; designate tracks for engagement. Detect and track ballistic missiles near midcourse (postboost and pre-reentry) to relay track data to land-based interceptors or to engage with ship based terminal phase interceptor missiles. Signals intelligence sensing and emitter location and identification, apply electronic disruption, denial, exploitation, deception, and attack mechanisms. Monitor adversary IO activities (MILCOMM, broadcast, social media, etc.) and issue approved IO responses.

SeaVision data, fusion service

Perform surveillance, search, detection, track, and localization of submarines for attack. Systems can be deployed on or from individual platforms (surface, air, underwater, space) and ASW operations are carried out by individuals or coordinated groups of platforms. Protect cyberspace and the electromagnetic spectrum from adversary attack, detect attacks, locate threat vectors, respond, and mitigate effects.

Undersea warfaredecision support system (USW-DSS)

AEGIS Combat System; surface search radar systems AEGIS Ballistic Missile Defense System Ships signal exploitation equipment (SSEE)

CANES SIEM (security information and event management) Meteorological Acquire, analyze, and predict the elements of the Global command and and physical environment that affect naval warfare, acquire, control systemoceanographic analyze, and predict the elements of the physical maritime (GCCS-M) systems (METOC) environment that affect naval warfare.

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• Core functions used across the enterprise: (e.g., content management (storage, search, discover, archive), multilevel security, cyber security, visualization); • Data standards for: • Tagging data (source, time, modes, target ID assigned, etc.); • Tagging security (source security level and aggregated or fused data security level) for each maritime object of interest level; • Metadata describing the pedigree of data; • Discovery of services and data enterprise-wide. • Security standards for network defense and cybersecurity; • Messaging and collaboration; • Standards and specifications by hardware and infrastructure providers and software applications. Even small components of functionality may be standardized as building blocks that can be combined on the fly to support rapid-response operations and the insertion of new capabilities. The implementing process to modularize these functions to be shared and reused is the service-oriented software architecture implemented in cloudbased computing environments [2]. And the extension of service orientation functionality over a distributed naval fleet afloat and it support elements ashore requires a robust information network infrastructure. The next paragraphs introduce these capabilities. Information Environment

A robust and secure network infrastructure, from seabed-to-space, is required to realize comprehensive ISR across distributed maritime assets. In the U.S. DoD this is referred to as the global information grid (GIG), then the DoD information network (DoDIN) and more recently has been referred to as a component of DoD cyberspace in Joint Publication 3-12, Cyberspace Operations, June 8, 2018. The network is formally defined as: The globally interconnected, end-to-end set of information capabilities for collecting, processing, storing, disseminating, and managing information on demand to warfighters, policy makers, and support personnel. The GIG includes owned and leased communications and computing systems and services, software (including applications), data, security services, other associated services, and National Security Systems. Non-GIG IT includes stand-alone, self-contained, or embedded IT that is not, and will not be, connected to the enterprise network [3].



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Like business enterprises, the military information enterprise, of which the network infrastructure is core, includes all information resources, assets, and processes required to achieve an information advantage and to share information across DoD and with mission partners; this enterprise includes [4]: • The information itself (e.g., sensor data, processed data, fused intelligence, sensor, and process management data); • The processes associated with managing information to accomplish the mission and functions (e.g., ISR sensor processing, sensor management, fusion, visualization); • Activities related to designing, building, populating, acquiring, managing, operating, protecting, and defending the information enterprise; • Related information resources such as personnel, funds, equipment, and IT, including internal use software and national security systems. The layers of the information environment and the location of high-level ISR elements (Figure 4.1) are like those in any large business enterprise; in the business case ISR is like marketing and sales roles (marketplace awareness and customer interaction-delivery). But the analogy is limited; business enterprise operations are optimized for profit with acceptable loss factored in—a dollar loss has the same value as a dollar gain. This is not true for naval operations in general and certainly not for naval ISR where a missed or misidentified target can be catastrophic. The vertical bars in the architecture refer to core services (mission assurance and information management) that cross all layers (e.g., security services such as encryption for protecting data are common at all layers). Service-Oriented Architecture

In modern navies, the evolution of such enterprise-level ISR systems has resulted in the adoption of service-oriented architecture (SOA). With a SOA, the

Figure 4.1  Conceptual Layers of the IE and the ISR applications.

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enterprise ISR workflows are performed by services; each service is identified, its function is explicitly specified, and it can be discovered (computationally) by other services and integrated dynamically into a processing flow. The services create a data flow by asynchronous message (and data) passing across a distributed SOA computing system. For ISR fusion, this means that the core fusion functions (e.g., GEOINT object extraction, data alignment (in space, time), correlation, and association, SIGINT-GEOINT fusion, or SIGINT extraction) can be assembled into different processing chains (or workflows) and the services tailored (parameterized) for different fusion problems, yet the core services remain standard [5]. Structuring the overarching ISR enterprise in this manner results in several benefits (Table 4.3).

Table 4.3 Main Features and Benefits of SOA Features Service

Characteristics and Benefits to ISR Processing Workflows ·· Service provision provided by a federation of common resources; ·· Services enable improved information flow in distributed computing environment; ·· Ability to expose internal functionality; ·· Workflow flexibility as one process of an ISR workflow may be executed in resource (Service A) while another process may be executed in a different one (Service B). Service reuse ·· Lower software development and management cost as common services are reused by many workflows; ·· Commercial service providers act in an open market by advertising their services; end users select providers based on their offerings. Service ·· Ability to develop and integrate new ISR functional capabilities rapidly by composition defining new service sequences using a set of exiting services. Service ·· Ability to optimize performance, functionality, and cost by a simpler introduction discovery of system upgrades (new services). Service ·· Services communicate by message passing and are therefore loosely coupled interaction (operating systems and programming languages are hidden to other services); ·· Loosely couple interaction provides flexibility, replaceability, scalability, risk mitigation, and fault tolerance; ·· Web services provide a messaging between distributed software services using a common platform and language independent standards (e.g., HTTP, Web Services Definition Language (WSDL), Simple Object Access Protocol (SOAP), and Extensible Markup Language (XML)) [6]. Asset ·· Services can integrate (wrap) existing code assets to apply legacy functionality. wrapping Message ·· Performance measurement measured by message efficiency; monitoring ·· Security attack detection can be performed by message activity monitoring. Message ·· Message management policy can control message throughput, routing; control ·· Application of security policy can prevent message attacks and provide data confidentiality and integrity. Virtualization ·· Improved reliability by implementing services in virtual machines (VMs); ·· Ability to scale operations to meet different demand levels.



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In the next section we introduce the information environment and illustrate the operation of an ISR workflow across services in a SOA environment, describing the asynchronous message passing.

4.2  Information Systems Fusion The distributed architecture provides responsive, and timely information because of the integration of interoperable services and information that is shared across those services. The approach also allows navies to leverage significant commercial industry IT investments in the technologies associated with SOA. Of course, this ability to integrate (fuse) information contributes to the objective of achieving a decision advantage and enabling distributed maritime operations and lethality. Commanders at all echelons require information that is critical to their understand of the maritime situation and their options (courses of action) for ISR and C2. Table 4.4 provides examples of many of these questions for the Kandigan Straits scenario in this book. These are based on typical intelligence preparation in the maritime operating environment [7]. The questions are organized by three of the levels of the JDL fusion model, introduced briefly in Chapter 1 and described in greater detail in the Chapters 6 and 7. To answer these questions in a comprehensive manner the information architecture (distinguished from the IT architecture in the previous section) is the structure that organizes, assembles, and labels content in an effective and sustainable way to perform the maritime mission: • Organize content: Establishes the ontology or taxonomies and hierarchies of information, such as sensor and source data types (e.g., sensor measurements and human source text reporting); • Assemble content: Defines the mechanisms to structure data in data stores, to age the data (when to archive or dispose of data), to discover and access the data; • Label content: Provides the format for labelling data in storage and transmission. This includes defining metadata standards for ISR data at the source (sensor time-of-collect, sensor ID, sensor mode at time-of-collect, sensor pointing, platform latitude-longitude, etc.). Stovepipe ISR systems maintained an information architecture that addressed the functions enumerated in Table 4.2 for each domain stovepipe. An integrated systems requires a common information architecture to allow infor-

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Warfare Domain Space

Air

Human/ social

Object (Target) Refinement (JDL L1) ·· Commercial and adversary overhead tracks and next observations windows (EO, IR, RF); ·· Blue critical Comm. and nav. space assets; ·· Adversary counterspace assets (land and space) [8]. ·· Aircraft at Red military airfields and use of commercial airfields; ·· Red military aircraft tracks; ·· Potential supporting air tracks.

All-Domain Situation and Domain Situation Threat Assessment (JDL L2 Assessment (JDL L2) and L3) ·· Which Blue force Threat: vessels may be ·· What access does Red have observable by Red? to space assets (commercial, ·· What is the maritime allied partners, national coverage of space technical capabilities) that assets by Blue? expose Blue operations? ·· Are Blue space assets What are the limitations of at-risk, and what would Red space access? be the impact? ·· What comms are red dependent on for space ISR? ·· What is the Red air force composition, readiness, and operations? ·· What are potential red air operations and abilities to support maritime COAs?

·· Locate major ashore ·· Is Red exploiting media media influencers to influence Green and sources of public public; how? opinion. ·· What are the main channels used?

All-domain situation: ·· What is the Red COA? ·· What is the timing of the COA and what are their critical decision points, their vulnerabilities to achieving objectives? ·· Does ISR support and describe the range of anticipated COAs? All-domain threat: ·· Is there a coordinated offensive? ·· When will they likely attack? ·· Where is the deception? ·· Which domain is at the greatest risk? ·· Is the air and surface maneuver coordinated for a first-strike engagement window? ·· Are the media and military operations coordinated? To what end? How can the public media influence be countered? ·· Why is the cyber-attack occurring now? What is the timeline? ·· What do all domains of activities tell us about the COA?



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Table 4.4  (continued) Warfare Domain Cyber and EW maneuver

Object (Target) Domain Situation Refinement (JDL L1) Assessment (JDL L2) ·· Detect and identify ·· Are cyber and EW EW activities and maneuver actions sources (disruption, coordinated? denial, deception); ·· What are the targets of ·· Cyber vectors and these actions, and likely sources. next COAs? Surface ·· Search, find, ID, and ·· Where is the missing track all Red surface combatant vessel? combatants and ·· How many vessels went support vessels; dark? ·· Focus search (all ·· Have they deployed any domains) for lost or SUVs? Where? dark combatant or ·· Where is the adversary potential-combatant missile range envelope? vessels; Relative to current Blue ·· Search (surface, air) forces? for small signature ·· Where are the USVs conducting commercial vessel reconnaissance or clusters that may relay. confuse ISR? Subsurface ·· Search, find, ID, ·· Why 2 subs to the and track all Red north? undersea vessels. ·· Is there a third sub deployed? Where is it? ·· Have they deployed any UUVs? Where? Seabed ·· What are the ·· Are we in range of vulnerable seabed any known adversary assets in the area of seabed sensors? ps (AO)? ·· What is the latency to ·· What are seabed Red seabed sensors? threats, sonar nets, mines?

All-Domain Situation and Threat Assessment (JDL L2 and L3)

·· What is the threat posed by seabed sensors; what is their range and detection ability? ·· What if we have been detected by seabed sensors; what countermeasures are available?

mation from all domains to be collected, stored, processed, and disseminated effectively. Consider the large architecture adopted by the U.S. Navy in transitioning from ISR stovepipes to an integrated information and IT architecture [9]. The architecture has the following characteristics, moving from the top, down to a more detailed level: • The IE allows the afloat forces to gain access to national intelligence, operational intelligence (OPINTEL), and tactical intelligence (TACINTEL) on a worldwide scale.

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• Ashore nodes include networked MOCs, big data centers, major analytic centers, and METOC centers. These nodes communicate to deployed afloat naval units via satellite communications. The nodes interface with the U.S. Intelligence Community collection sources by the ashore facilities. The Primary Ocean Prediction System (POPS) combines worldwide METOC data to provide weather prediction and environmental models to deployed fleets. • Afloat fully capable nodes provide afloat force units with an organic node with maneuver command, battle management, track management and effects analysis capabilities. These nodes maintain computing, analytics, and network distribution management capabilities. They are maintained on major force units (carrier or expeditionary strike groups, Joint Force Maritime Component Command groups (JFMCC)). • Afloat limited nodes are local computing services that include effects analysis, track management, and battle management capabilities for their mission (e.g., CG or DDGs on independent operations or missile defense operations). • The afloat node uses a common core, the CANES, to provide ISR services with plug-ins (application programming interfaces (APIs)) for DCGS-N, as well as cyber situational awareness (NCSA), Maritime Tactical Command and Control (MTC2), Navy Integrated Tactical Environmental System (NITES2), and Naval Operational Business Logistics Enterprise (NOBLE). The NOBLE family of systems is composed of the Naval Operational Supply System (NOSS), Naval Aviation Maintenance System (NAMS), and Naval Operational Maintenance Environment. These capabilities illustrate the breadth of integration of systems using common services that are cyber secure and auditable. • CANES provides common hardware and software services in a SOA architecture across these mission capabilities; all benefit from common data strategy, standards, and information security. • The Distributed Common Ground System-Navy (DCGS-N) is a family of intelligence systems (FOS) that is interoperable with the other service and agency DCGS systems and provides multilevel security. It includes collection management, geospatial and signals intelligence analytics, display, fusion, and dissemination services. It also provides reach-back capabilities to MOC support centers. • Closely associated with DCGS services are battle management systems dependent on precision target locations derived from multiple sensors. Battle management encompasses many combat systems, for example:



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Aegis Combat System, Joint Land Attack Cruise Missile Defense Elevated Netted Sensor System (JLENS) services, air operations battle management services, IO and electromagnetic warfare (EMW) services that provide the rapid-response capability to integrate kinetic and nonkinetic (electronic warfare, directed energy) fires. This naval cloud-based computing enables the development and deployment of service-based workflows for ISR tasks using the common core services, as well as custom services unique to a mission area. Yet the incremental deployment of such a large enterprise is measured in decades because of the scale and scope of sophistication. U.S. Navy pioneering concepts were developed in the late 1990s (NCW, ForceNet), development was initiated in the early 2000s, and incremental deployment was in the following decades [10]. We now consider the example of an ISR fusion workflow that combines sensor and source data. The ISR fusion capability seeks to maintain an operational picture of the objects of interest in the maritime operating environment (from seabed objects to overhead satellites) that addresses these questions needed by the vessel commanders and the fleet commander. The roll-up of the information to answer the questions in Table 4.2 is provided by the fusion process with ISR. We now drop down to a deeper level of description to illustrate the process of coordinating services to perform a basic function, using a Unified Modeling Language (UML) diagram to step through a service sequence. The operation of basic maritime fusion process for surface ships only is illustrated in a UML sequence diagram (Figure 4.2) that shows one cycle of a series of fusion services that update the situation, threat, and tasking to ISR sensors [11]. The set of services are organized across the top and the lines with arrows that proceed from service-to-service show the messages and data flow that sequences through the data fusion process. Note that the arrows are transactions across the digital network and may be routed through many nodes to arrive at the destination service. This process shows the data-driven sequence that evokes services when a new set of sensor observations (from one or more MultiINT sources). A similar diagram could be shown for query-driven services that are evoked when an ISR operator queries the systems to analyze data in the object-situation data store to answer a commander’s question. Note also how the sequence illustrates the asynchronous nature of the process, and how the services can be distributed across compute platforms and even across physical platforms. The circled numbers in the figure correspond to the paragraphs that follow describing the sequence:

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Figure 4.2  Sequence diagram of SOA-specific scenario.

1. New source data from ISR sensors (a batch from a single sensor, or multiple sources) is presented to the data aggregator service. In this example, the batch contains objects (surface vessels observed by sensors) and extracted from surface search radars on ships and aircraft, and RF signal intercepts of maritime radars, as well as EO and SAR ship detections from commercial satellites. The data aggregator sends the batch to the object refine service. Figure 4.3 illustrates how just



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Figure 4.3  Service-to-service message flow over TCP/IP network.

2.

3.

4.

5.

the first message may be relayed from the ISR aggregator service to the object refinement service through the standard internet using the TCP/IP Protocol network to reach the object refine service. The object refine service aligns the object observations to a common time and space reference. It also requests (GET_Obj) the current objects in the area of coverage of the new objects from the object-situation storage. Those objects are retuned (RTN_Obj) from the storage and a correlation measurement is computed to determine if the new objects can update the current objects in time or location and identify or track dynamics. This service may also update the track of objects (a moving ship) using a tracking algorithm to associate a new object with an existing track, and update the track. If an RF ellipse can be uniquely associated with an object or track, the ID can also be updated. If satellite observations can be associated with any tracks, identity may also be updated for the tracks. The updated object data message (Update_ Obj) is then sent to the object-situation store to update its state. Note that the object-situation storage is the source of the common operating picture presented to commanders, and it is used by C2 simulations (explained in Chapter 8) to assess alternative COAs. The completion of object updating from this batch is reported to the data aggregator service; and it requests and update of the situation assessment, based on the new state. Next, the situation assessment service requests the current situation (GET_Sit) and, when retrieved, estimates the change in situation based on the new objects, updated tracks, and their relationships. (This process is described in greater detail in Chapter 6.) The updated situation is sent to the object-situation store (RTN_Sit).

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6. Once the situation is updated and reported to the ISR data aggregator, a REQ_Thrt is issued to the threat assess service to determine if changes to the situation pose a threat (or offensive opportunity) to the fleet or its mission. After issuing a GET_Sit and receiving the newly updated situation (RT_Sit) the threat assess service assesses the implications of the maritime situation and projected outcomes from alternative COAs. By requesting a maritime surface simulation (REQ_SIM) from the simulate service. 7. The simulation service explores the space of current and possible future situations over a defined time horizon (e.g., 12 hours) and returns results (RTN_SIM) to the threat assess service. 8. The threat assess service evaluates (a self-request loop) the potential threats and issues an Update_Thrt report message to the object-situation store, as well as the ISR aggregator service. 9. The aggregator service finally requests an update to sensor tasking (REQ_Tasking) based on the new situation and identified threats that may change priorities in the ISR network. The resource management service requests the current situation/threat profile (GET_S/T) and then computes an updated ISR strategy, sensor allocation, and collection schedule (tasking) based on the updated situation. The updated tasking (Update_Tasking) is sent to the object-situation store and to the ISR aggregator, which forwards it to the appropriate ISR sensor systems. Note that this simple example sequence of services only maintained custody of surface ship threats; a complete seabed-to-space process would maintain an object-situation store of the complete domain of objects, the full domain situation, and the threat imposed by the aggregate behavior of all objects.

4.3  Naval C4ISR Challenges While the preceding sections have outlined the enterprise and information level concepts for implementing a large naval ISR capability, there remain many challenges to achieve the scope and scale of such a large enterprise. This is because, of course, it is not just ISR, but all application systems and the infrastructure itself that must be translated from stovepipes to the distributed computing environment. We summarize the challenges in the areas of computational implementation and operational transformation. Computational implementation challenges are as follows:



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• Investment and planning for transition. Of course, any navy with a large investment in legacy ISR stovepipes must develop a transition strategy with incremental delivery milestones, migration paths, and budgets. These must guide the introduction of new IT infrastructure, hardware, software, and translation of legacy capabilities into services (breaking apart, wrapping, expanding to a shared service useful across ISR mission threads, etc.). • Governance policies and standards for data and services. These must be established, configuration managed, and adhered to by all components. In addition, methods must be developed to continually audit SOA systems to assure compliance. • Services sequence profiles and metadata management. Methods must be established to design, document, manage, and test the large number of messages exchanged between service for all ISR workflows. Profiles must be developed to enforce compatibility with standards. • Bandwidth management. The asynchronous and message-passing characteristic of SOA also requires monitoring and management of the capacity-limited channels in the network. • Security. Information assurance and security standards must be established to support all services; application-level security alone is not appropriate in the SOA environment which is reliant on message passing across applications using many layers of the computing stack. Operational transformation challenges are as follows: • ISR discipline organization. The movement from stovepiped ISR disciplines to all-domain introduced the significant challenge to perform distributed all-domain analysis ISR rather than independent analysis of air warfare, ASW, surface warfare, electronic warfare maneuver, and more as independent disciplines. This will also increase the complexity of information provided to afloat operators. While the single domain analysis disciplines will remain, all source analysts will address coordinated adversary actions across all domains. For example, preparations for a coordinated precision air-missile and cyber strike must be recognized by a warning analyst monitoring all domains. The ISR disciplines (e.g., collection managers, sensor operators, data analysts, intelligence analysts, operators, and targeting analysts) will address the broader capabilities of ISR and be able to use tools to that integrate information across all INTs.

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• All-domain concept of operations (CONOPS). The concept of operations for performing analysis and targeting across all domains will also require new concepts to enable cross-domain coordination, tight integration of collection and analysis, and rapid delivery of targetable intelligence to C2. The operational concept of optimal maneuver requires an understanding of the high-dimensional operating space (space, air, surface, undersea, cyber, etc.) and the CONOP must enable all to comprehend this; it is much larger than the physical common operating picture of the past [12]. • Training. Of course, with discipline and CONOP challenges, training will require tailoring to enable the disciplines to teach broader knowledge, develop deeper skills in many complex areas, and apply more critical thinking abilities to address the large domain of problems they will face. The following chapters move toward the implementation of the ISR process providing more detail on the required data fusion functions.

Endnotes [1] NATO is developing ISR technologies similar to those described in this chapter. See NATO Science and Technology Organization 2021 Highlights, “Interoperability and Networking of Disparate Sensors and Platforms for Tactical ISR Applications,” pp. 39–40, and “Data Environmental Knowledge and Operational Effectiveness (D-EKOE)” pp. 41–42, https://www.nato.int/nato_static_fl2014/assets/pdf/2022/4/ pdf/2021-NATO-STO-Highlights-web.pdf.

The Royal Australian Navy (RAN) is studying robotics, autonomous systems, and AI (RAS-AI) technologies directly applicable to naval ISR applications. See Slapakova, L., P. Fusaro, J. Black, and P. Dortmans, “Supporting the Royal Australian Navy’s Campaign Plan for Robotics and Autonomous Systems: Emerging Missions and Technology Trends,” Santa Monica, CA: RAND Corporation, 2022, https://www.rand.org/pubs/research_reports/RRA1377-1.html.

[2] Here, we only introduce the SOA concept as an enabler for ISR fusion; for more depth, see Erl, T., Service-Oriented Architecture: Analysis and Design for Services and Microservices, Pearson, Dec. 2016. [3] Joint Publication 1-02. [4] Management of the Department of Defense Information Enterprise (DoD IE), Department of Defense Directive 8000.01, March 17, 2016. [5] By core fusion services, we refer to the most elemental functions that make up each of the fusion levels. For example, level 1 fusion refines individual objects from raw data (e.g., targets in imagery). The core functions of level 1 for a single source include object detec-



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tion in raw data, object extraction from raw data, alignment of the extracted object in space and time to a common reference, correlation of the object with other objects and association of the object measurement with other object measurements, combination of data from associated measurements, dynamic tracking of object kinematics, and more. [6] “Web Services Description Language (WSDL) Version 2.0 Part 1: Core Language,” W3C Recommendation June 26, 2007, http://www.w3.org/TR/wsdl20. [7] Joint intelligence preparation is a continuous process. See JP 3-0, Joint Operations, 17 January 2017, Incorporating Change 1; October 22, 2018, p. xiv. See also Cross-Domain Synergy in Joint Operations Planners Guide, Joint Staff Force Development J7; January 14, 2016; https://www.jcs.mil/Portals/36/Documents/Doctrine/concepts/cross_domain_ planning_guide.pdf?ver=2017-12-28-161956-230. [8] See “Challenges to Security in Space,” Defense Intelligence Agency, January 2019, https:// media.defense.gov/2019/Feb/11/2002088710/-1/-1/1/SPACE-SECURITY-CHALLENGES.PDF. [9] Tim Anderson, Battlespace Awareness, and Information Operations Program Office (PMW-12) for Intelligence Analytics 2018 Meeting (Brief ), PEO C4I, Approved for Public Release unlimited, February 7, 2018. [10] The historical background to NCW and ForceNet is provided in the introduction to Chapter 7. [11] The Unified Modeling Language (UML) specifies diagrams that represent SOA service sequences and messages to aid in design and analysis. The sequence diagram is a model (abstract representation) that is used in model-based engineering. The example is a very simple, but illustrative, description of the basic data fusion process. See Unified Modeling Language (UML) Specification Version 2.5.1, OMG, December 2017. [12] For an Air Force perspective of this challenge, see Lingel, S., et.al., “Joint All-Domain Command and Control for Modern Warfare: An Analytic Framework for Identifying and Developing Artificial Intelligence Applications,” RAND Report RR-4408/1-AF, 2020.

5 All-Domain Fusion and Operation Challenges The introduction of the term all-domain has created the vision of integration of technical and operational activities across several domains—spheres of activity or influence with common and distinct characteristics in which a force can conduct joint functions [1]. The term multidomain also exists to describe systems that operate across multiple domains, but all-domain emphasizes full integration and synchronization across the entire spectrum of conflict. At the U.S. DoD level the broadest application is in a joint all-domain operations concept that is comprised of air, land, maritime, cyberspace, and space domains, plus the electromagnetic spectrum [2]. Actions by the joint force in multiple domains integrated in planning and synchronized in execution, at speed and scale needed to gain advantage and accomplish the mission [3]. Ultimately, all-domain seeks the coordination and optimization of everything, everywhere. In this book, our focus is on naval maritime and littoral operations; the emphasis is on integrating and orchestrating the ISRT sensor and C2 operations for naval and marine forces. While these forces must coordinate with air, space, and land forces as well as intelligence services, the focus here remains on naval operations [4]. Coordination of sensing (ISRT), command and action (C2) across all of the domains of naval operations is envisioned to enable surprise, the simultaneous and sequential application of force at enemy centers of gravity, and the ability to gain both physical and psychological control over the operational environment. The key domains we focus on in this chapter include maritime, air, space, and cyber. The all-domain concept requires a significant degree of technical and operational requirements to achieve the desired superiority in information across 87

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the domains, the effectiveness of decision making across those domains and the impact of coordinated force application. The driving requirements include: • Operational requirements: (1) Coordinated planning and understanding of the expected effects of synchronized C2 and force application (e.g., physical and psychological effects), (2) distribution of command intent to allow decentralized and adaptive execution of forces, and (3) speed of decision, execution, and assessment to conduct coordinated long-range engagements by the distributed platforms. • Technical requirements: (1) A global scale information network (also called a grid or mesh) then enables the rapid and secure exchange of sensor data, sensor control, and C2 data among participant across domains, (2) data fusion capabilities to correlate, associate, and combine information across domains to sustain a timely and accurate operating picture across domain, and (3) decision support to aid commanders to understand, maneuver, and apply combat power appropriate for the military objectives. The challenges all relate to the ability to carry out the maritime ISRT sequence that moves from surveillance-to-targeting that follows a typical sequence of states [5]: Detection: The wide area surveillance process performs area scanning and directed area searches (often based on cues from other sources such as a port-departure notice) with the objective of detecting targets. For surface search, vessels are detected by zone of operation, and are categorized as cooperative (reporting ID by declared ID on the AIS per international law), or dark (detected but not emitting or identified—unknown vessel). For air search, aircraft targets on radar or targets emitting radar or other signals detected by ELINT are detected. Identification: Once detected, surface vessel must be classified by vessel type and uniquely identified (e.g., by hull, name) to declutter the surface picture. If cooperative AIS data is not associated with a vessel, other noncooperative methods such as vessel RF signal fingerprints (specific emitter identification (SEI)) must be used. For aircraft, efforts are made to identify the target by commercial track and transponder reporting, identification friend or foe (IFF) interrogation, flight behavior, flight path, point-of-departure (airfield), or other noncooperative means. Identification is critical to understand both the surface and air picture, and the potential implications of an adversary’s combined air-surface maneuvering. The ISR system must assess the risk of target behaviors, including the



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vessel history and particulars (ownership, cargo) for identified vessels and unidentified vessels. This state involves performing follow-up tasking to sensor systems to resolve identification and initiate tracking. Track: The next state of surface or subsurface vessel ISR is to maintain a track throughout passage in an area of interest—also known as maintaining custody of the vessel. This requires the sustained observation of the target by continuous or periodic sampling to assure the target is not lost in clutter. An emitting AIS target, easily tracked, may go dark, causing the ISR system to lose track if it cannot switch to another sensor mode to regain custody. Targets in-track are correlated across sensors and integrated to provide a common operating picture (COP). Air targets likewise require a sensor (e.g., radar) to sustain periodic observations (e.g., radar returns) of the target at a higher rate than search to maintain a track of the aircraft trajectory. The track can be represented by a series of observed POSITs or a computational tracking model (e.g., a Kalman filter) that smooths the POSITs, estimates the trajectory parameters, and predicts the expected location of the next sample observation. Throughout tracking, the ISR system monitors track behavior for anomalies to alert to unusual or threat-like behaviors, or maneuvers to risk loss of track. Targeting and engagement: The nomination of a target for targeting refers to the selection of a target for action. This action may be to intercept a vessel for reconnaissance or boarding-inspection, or for committing a weapon to engage an air or surface target. The following four sections (Sections 5.1–5.4) describe the challenges that these requirements impose. The challenges fall into four areas. The first area includes the effects of the distribution of sensors, C2, and weapons across spatial distances, and the scale of the network. Travel time for information across these distances introduces latency (time delay) that must be addressed by any networked system. But latency is also introduced in traffic as network scale increases because network congestion and interference also contribute delays. Latent data arrival to data fusion nodes has the potential to introduce significant data association errors. (This topic is addressed in greater detail in Chapter 7; distributed data fusion systems must be able to adapt to the inevitable latencies in networked systems.) The second area of challenge is the ability to maintain an understanding of the dynamic situation when using many sensors. The sensor is effectively sampled when performing surveillance and tracking of maritime targets and the required update rates are inherently related to the rate of change of the dynamic targets. While the first challenge areas are based on spatial and temporal requirements, the third area is driven by the measurement accuracy demands

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of fire control systems to deliver physical and cyber weapons. The fourth area addresses the need for integration of cyber capabilities to assure commanders understand adversary coordination of attacks in this domain with the physical domain. These challenges in the following sections inherently call for speed and precision, and in turn, may necessitate degrees of automation and augmented cognition to achieve high levels of performance. The potential contributions of automaton and augmentation are further described in Chapters 6, 7, and 8.

5.1  Challenge 1: Spatial Distribution, Association, and Latency The distribution of maritime operations distributes naval platforms and therefore the sensors, weapons, and communication relays are spatially distributed at wider distances than traditional battle groups formations or carrier groups that operate in clusters to concentrate offensive fire power, defensive measures, and overlapping sensors. This spatial distribution contributes following operational challenges to ISRT: • Organic sensing gaps: The wide distribution of surface platforms introduces gaps between platforms eliminating organic sensor overlaps (surface and air radar coverage, towed array acoustic coverage). These large gaps then must be covered by nonorganic sensors to prevent vulnerabilities to air, surface, and submarine threats. Consider the simple example (Figure 5.1) that illustrates the relative density of fleet vessels in three cases: (a) a carrier battle group tightly spaced for concentrated force, (b) the same force distributed over a moderate distance, and (c) same force widely distributed with very large gaps in sensor coverage. The demand for ISR coverage to monitor these gaps can be significant—especially in seas where heavy commercial traffic creates significant clutter to challenge the ability to maintain custody of adversary ships and submarines that enter the operating area. It is reasonable to consider, in the wide case C, that the fleet is distributed over a vast 250 nm (radius) circular area of operations with an area coverage of 200,000 nmi2 [6]. In the figure, we consider a DMO fleet (uniformly distributed) consisting of 1 carrier strike group (1 carrier, 8 destroyers, 2 maritime patrol aircraft, 2 air surveillance aircraft, 5 long-range UAVs, 10 deployed Seabed Sensor networks, 10 UUVs, 10 USVs, and 2 attack submarines). There are 31 vessels in this fleet and 9 available air assets; 5 in the air at any time; this results in 36 fleet sensing platforms. Figure 5.1 also considers larger fleet sizes to show the intervessel distances for 72 and 108 platform fleets.



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Figure 5.1  Vessel density at three levels of distribution.

• Distributed sensing: DMO necessitates a greater reliance on distributed and cooperative sensing, where sensors must communicate and share their reports while enabling control of their operation from distant users. The concept of organic sensors (sensors controlled by the host platform) must give way to shared and orchestrated sensing to provide a common operational picture for all. • Domain distribution: Of course, the breadth of operations from the seabed to space introduces additional dimensions of distribution beyond spatial distribution. Attacks will be coordinated across domains to create a maximum disruptive effect, or a highest quality (believable, seductive) deception. For example, cyber and seabed sensors must be coordinated to distinguish anomalies in reported sensor data and disruption of network channels that may indicate a coordinated attack on a critical undersea sensor network and its associated communication channel. • Target dispersal and weapon range: DMO also causes opposing naval forces to distribute more widely to confront the distributed fleet, and this in turn demands greater ISR coverage to monitor the threat and longer-range weapons (missiles, UAV loitering, etc.). The DMO force is driven to apply longer-range missiles, for example, to engage targets well beyond the horizon, and beyond organic sensor ranges. These longrange missiles (Figure 5.2) must rely on cooperating nonorganic sensors to enable fire control for the missile in flight. We discuss this further in Section 5.3.

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Figure 5.2  Long-range ASM trajectories.

These operational factors, due to the spatial distribution of targets and sensors, introduce technical challenges that we address throughout this book. The distribution of naval vessels (and the adversary distributed response) introduces two key challenges that must be addressed by technical solutions: 1. Latency: The networking of sensors to relay nodes and to fusion nodes, and then to weapon nodes, introduces time delays—latency between the time of detection of a target and time reported. As distribution increases, and network scale increases, even latency increases. Latency does not increase linearly with distance, but nonlinearly with the paths and nodes across a network. For example, the path from an autonomous surface sensor to a communication relay network of LEO satellites and then to a local destroyer may vary depending on the location of satellite relays. The solution to this challenge is a dynamic network that maintains shortest-path communication from sensors to fusion nodes, by adapting as sensors and fusion platforms move; of course, high-speed routing of the data in the network is necessary. 2. Report association: The latency between detection and reporting, and the distribution of sensors reporting at different observation (or sam-



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pling) times poses a challenge to properly associating reports—assuring that reports are properly assigned to common targets [7]. The solution to association is based on assuring appropriate sampling rates and applying context and target signature data (e.g., shapes, signals, and behaviors) to assure the correct matching of data across sensors and from observation to observation. We discuss the technical approaches to perform association in Chapter 7.

5.2  Challenge 2: Temporal Sample Rate and Dynamic Targets Naval vessels (ships, submarines) and naval aircraft maneuver—they are in continual readiness to maneuver dynamically as an element of fleet tactics in war. Captain Wayne Hughes emphatically points out: [Naval] warfare is deadly conflict. Tactics, as the devices by which battles are waged, are conceived, and executed at the center of this violence… They are more visceral in the execution than policy, strategy, operational art, or logistics…tactical plans and battlefield decisions are influenced by an environment of controlled violence and directed chaos… At sea the predominance of attrition over maneuver is a [basic principle]… Forces at sea are not broken by encirclement, they are broken by attrition [8].

Hughes points out that naval warfare is so destructive and decisive that the avoidance of it altogether weighs heavily on strategists. For this reason, fleets are dynamic and maneuvering at peace and war to avoid detection and tracking and to prepare for immediate action. Therefore, a pillar of naval strategy is the necessity that ISR capabilities maintain continual custody of the location, movement, and activities of opposing naval forces (in-port, maneuvering at-sea, hiding on-station, and inmaintenance). For the naval fleet conducting DMO operations this demands a persistent tracking of adversary fleets and knowledge of the commercial maritime clutter in which they operate. By tracking, we refer to the ability to maintain a record of the successive positions of a moving object. We distinguish the course that may be planned by the object (e.g., a commercial shipping route) and the track that is the actual path or trajectory followed by the object. It is helpful to further distinguish two primary methods of performing the tracking process (Figure 5.3): 1. A time series of POSITS records the track of a ship, for example. The common maritime track produced by a series of AIS POSITS can plot, in near real-time, the trajectory of a ship. AIS Class A transponders typically provide a new location update every 2 to 10 seconds while

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Figure 5.3  Approaches to physical target tracking.

underway and every 3 minutes while at anchor, enabling a persistent track by connecting the POSITS with a mathematically smoothed curve. 2. Computational track models are developed for ships and aircraft by filtering the observations from radars and other time sampled sensors. The filtering mechanism (also called a tracking loop) develops over time a computational model of the behavior of the target under track. In Figure 5.3, a typical filter mechanism is shown where: (1) observations are received and compared to the model prediction for that observation, (2) the comparison provides the error in the model estimate, (3) an update of the estimated current state, and (4) a prediction of the next expected observation state. The computational model adapts as it estimates the current state, predicted next state at the time of next observation, and the model error (covariances between state variables) to provide a measure of the track quality. Several sophisticated estimator mechanisms have been developed and performance is heavily dependent on the sensor sample rate, the target dynamics (maneuverability), the density of targets, and clutter. Most estimators are statistical estimators that rely on a knowledge of the statistical properties of target dynamics [9]. Traditional maritime tracking is performed by organic sensors located on a surface ship looking out to the horizon to track at sample rates between 0.5



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samples/sec and 1 samples/sec (surveillance) or >10 samples/second (racking a maneuvering air target). The challenge for DMO is to perform tracking of targets at great distances, by distant platforms applying long-range networked sensors. These sensors may be on unmanned platforms (e.g., satellites, UAVs, and USVs). The degree of difficulty to perform tracking in these cases and the approach to sensing is a function of the target speed and the sample rate of observations. Consider the wide range of target speeds for important naval targets to track (Table 5.1); the table does not consider the maneuverability of each class of targets that must also be considered as turning and maneuvering poses an additional challenge to sustained tracking (and adversaries know this). It is important to recognize that the table is for physical kinematic targets that are important to the fleet; but nonphysical targets in the cyber and information domains are also critical. In these domains the fleet must also be able to track the flow of cyber-attacks and the propagation of information threats that spread through the cyber medium to influence human targets, decision making, and actions. We discuss this further in Section 5.4. There are many factors that influence the desired ISR sample rate, including the purpose of tracking, the interval between observations (the inverse of the sensor sample rate), target clutter density, and target maneuvering rate. We can illustrate the challenges by considering the guided missile frigate (FFG) ship track in Figure 5.4. Four different tracking challenges are posed to tracking as the vessel departs a naval base and heads to blue water to intercept a target. The FFG departs port (1) proceeds to a loiter area to perform a loop timing maneuver and (2) to rendezvous with an escort submarine before heading to Table 5.1 Relative Speeds of Targets to be Tracked

Target Container, commercial ship CVN (nuclear aircraft carrier) DDG (guided missile destroyer) Submarine (submerged) Fast missile corvette Aircraft (patrol) Cruise missile (subsonic) Aircraft (fighter) Hypersonic missile (Mach 7)

Desired Sample Max Velocity Rate to Track Max Velocity (ft/sec) (samples/min) 20–25 kts 33–42 fps 1 30 kts 50 fps 2 35 kts 60 fps 3 30–40 kts 50–68 fps 3 30–60 kts 50–116 fps 5 350 kts 600 fps 20 550–700 mph 800–1,000 fps 30 1,500 mph 2,200 fps 60 3,800 mph 5,575 fps 60–120

ASM cruise missiles such as Excoct or Harpoon. Hypersonic boost-glide vehicle and cruise missiles range from 5 to 7 Mach.

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Figure 5.4  Tracking an FFG on an operational intercept mission.

the operational area. Once the FFG and submarine are ready, the FFG departs, crossing the offshore shipping lanes and performing maneuvers close to merchant traffic behaviors tracks to confuse efforts to track the vessel (3). Once outside of the shipping lanes, the FFG proceeds at 30 kts to the operational area to prepare for intercept and engagement with an opposing force. In the process of that operation, the FFG and other combatant vessels joining the operation make several tight and crossing maneuvers (4) to evade tracking. Tracking the FFG in this scenario requires, first the detection of the ship departure (1) from the port (by AIS signal, seabed or surface sensors, or other ship, air, or space radar sensors) and initiation of a track process that requests persistent sequential sensing to sample the track at a sufficient rate to maintain initial custody. As the FFG loiters for rendezvous (2) the tracking may require increased sampling to keep up with the turns. As the FFG enters the ship lanes (3) and maneuvers back and forth to blend with merchant traffic to cause any tracker to follow a merchant ship and lose the combatant FFG. (This is also a period that the AIS may be turned off to cause the FFG to go dark, to AIS tracking.) Finally, after proceeding to the operational intercept point, the FFG may again perform sharp maneuvers (4) to cause a tracking process to break lock and lose custody. AIS reporting rates at 6–30 samples per minute provide excellent sampling and a high-altitude UAV may provide a similar sampling rate on this target, but OTH radars and satellite sensors may provide much lower sampling rates. Distributed sensor systems that integrate observations from multiple sensors, contributing at different times, may provide uneven sampling further complicating track custody. The relation between target separation and sampling interval on tracking performance for the FFG track in Figure 5.4 is illustrated in Figure 5.5 [10]. The ability to correlate samples is unambiguous when the ship is widely separated from ship traffic and is broadcasting a high-rate AIS signal. As the ship conducts loitering maneuvers in low density areas (Region 1) the performance diminishes if the sample rate cannot distinguish the maneuvering rate, and in high maneuvering at lower sample rates (due to AIS being turned off and observation rates from air and space sensors are reduced). In Region 4 the track



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Figure 5.5  Regions of correlation and tracking performance.

may be lost, unstable, or misclassified. Throughout the passage through the shipping lane (Region 3), the background clutter of merchant ships reduces the separation between targets and can cause incorrect assignment of observations to tracks and, ultimately, loss of track. In summary, the factors that influence tracking performance include: • The purpose of tracking: If the purpose is loose surveillance and custody over a long period, lower sample rates are acceptable, but higher sample rates are required for evading and maneuvering targets, and for targeting and engagement. • The interval between observations (the inverse of the sensor sample rate) is important to maintain track continuity and is a function of the target rate and rate of maneuver. • Target clutter density is the number of nontarget ships in the sensor field of view; high densities challenge the tracker to distinguish between observations when tracks cross or closely cotravel. • Target maneuvering rate can cause the tracker to lose track when an observation does not meet the predicted next observation criteria; this requires the tracker to rapidly adapt the behavior model to determine the target has abruptly changed behavior (e.g., cruise behavior to rapid maneuverings).

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These operational factors introduce three key requirements that must be addressed by technical solutions: 1. Sensors must provide coverage to enable sample rates sufficient to maintain tracks on combatant targets. This means that they must have sufficient field of regard and pointing agility to assure sampling across the anticipated density and dispersion of maritime targets. The technical solution requires a network of distributed sensors that can be coordinated to sample multiple targets in time sequence to maintain multiple tracks, adapting the sample rate as the targets transition from cruise to maneuver. 2. A robust network between sensors assures that sensors can be orchestrated to control pointing to targets, perform cueing and handoff from sensor to sensor, and provide paths for sensor data to appropriate fusion noes where data are associated. 3. Computation to perform sensor management for the functions of cueing, search, handoff, and tracking based on track models that guide sensors to the predicted next observation of a track.

5.3  Challenge 3: Accuracy for Fire Control and Missile Engagement Beyond tracking, the most stringent demand for accuracy of the state of a surface target and its predicted states are for fire control to enable DMO missile engagements. In this section we illustrate fire control configurations to conduct long-range surface to surface missile (SSM) engagements by networked sensor and command links, targeting by remote control from OTH. Traditional targeting has been performed by sensors that are organic to a surface ship. The ship’s surveillance radar may acquire an air or surface target by line-of-sight contact and then dedicate a fire control radar to acquire and track the target to provide a high accuracy, high sample rate track of the target. Using this track the missile is launched and the ship provides required inflight guidance to the missile through flight until the missile acquires the target with its seeker sensors. For OTH targeting in DMO, networked sensors must provide the sensing with sufficient accuracy to engage a distant target. The term integrated fire control (IFC) refers to the means to integrate fire control mechanisms (sensors, C2, engagement control, guidance) and CEC refers to the mechanisms that enable sensor platforms, firing platforms, and C2 platforms to conduct coordinated missile engagements (air and surface targets). In these cases, the demand for accuracy of the target track up to and after launch of the missile is paramount. The accuracy requirement:



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• Provides accurate coordinates of the target at time of launch and predicted location at time of missile arrival to optimize the trajectory to the target area; • Provides accurate inflight updates as the target maneuvers when it receives an inbound missile warning; inflight updates provide the predicted location at missile arrival. .

In this section three conceptual CEC modes appropriate for long-range surface engagement in DMO are illustrated. Figure 5.6 and the following paragraphs describe three operations of these modes using remote OTH sensor data from satellite, UAV, and USV sensor systems [11]. More modes of operation have been conceived including: Precision Cue (where a remote sensor provides a tip to a potential threat and cues other sensors to acquire a track and prepare to engage if appropriate; in Figure 5.6, this may precede all of the modes shown), Launch on Remote (where the launch is entirely based on remote data from single remote sensor or the composite of multiple sensors), and Preferred Shooter Designation (where firing units are selected from a group to optimize the engagement). While earlier concepts published were focused on air target CEC, here, we describe CEC modes suitable for DMO SSM engagements [12]. Mode 1: Engage on Remote. In this mode one or more remote sensors provide the cue to the remote target, as well as the identification and tracking data to enable commander decisions to engage the target. The sensors also provide the track update data to commit to the engagement and deliver inflight updates that are relayed to the missile throughout the engagement data upon which all (or portions) of an engagement is conducted. The relay network provides the sensor-to-fusion node data and relays the inflight data to the weapon. The fusion node combines multiple sensor measurements (also called composite data) into the common track and ID used for firing decisions and engagement. This mode requires the composite track to maintain fire control quality information (accuracy and accurate target maneuver projections) throughout. Notice that the remote sensors provide all data directly to the firing vessel. Mode 2: Forward Pass. In this mode, a C2 vessel controls the engagement, and remote platforms closer to the target (the middle panel in Figure 5.6, a UAV and USV) provide in-flight updates to the weapon. The missile is launched from a firing vessel and is handed off (forward passed) to the UAV and/or USV platforms to provide terminal guidance data. The remote sensor network (space plus UAV-USV platforms) provides fire control quality targeting data to firing unit. The firing unit launches the weapon by command from control unit and missile updates are provided by the close-in (and covert) unmanned platforms through the relay network.

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Figure 5.6  Conceptual CEC modes.

Mode 3: Remote Fire. In this mode, the firing unit is commanded to fire with direction from the C2 vessel and is not involved in fusing data or



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maintaining track of the remote target; it is provided the launch and target data. The remote sensor network detects the target, and the command vessel receives data and completely manages the fusion of data, sensor tasking, and engagement by the remote firing unit. Missile updates are provided by the remote sensors (e.g., UAV or USV) as directed by the control unit. As missile speeds move from subsonic cruise missiles to supersonic cruise and even hypersonic, the much shorter missile time-of-flight allows much less time for surface targets to maneuver (Table 5.2) and reduces the predicted track location at the time of missile arrival. For example, a ship traveling at 30 kts can traverse almost 10 miles in the time it takes a 700-mph cruise missile to traverse a 200-mi range-to-target. On arrival, the missile sensor (seeker) searches for the target and verifies signature attributes before guiding to impact. The accuracy of the inflight track updates is critical to enable the missile seeker to acquire the target in the endgame—the final 10s of seconds of flight. Demonstrations of long-range CEC surface-surface engagements in early 2021 by the U.S. and Russian navies were conducted to attack targets over 300 km by ship-launched missiles using cooperative engagement strategies [13]. The U.S. experiment used a blend of remote sensor data from manned and unmanned ships and aircraft to provide fire control information to a guided missile destroyer, enabling the launch of an antisurface missile from OTH to hit a target more than 250 miles distant [14]. Rear Admiral James Aiken (U.S. Navy) said of the demonstration, “We teamed manned and unmanned vessels together. We also used the fusing capability… It was totally passive where we didn’t have active sensors on target …We also look for space as well to identify the target and then once we found the target, we were able to track it because of the [electromagnetic signal] that was coming off the target, develop lines of bearing, then launched the missile” [14]. The implementation of these concepts requires the consideration of two additional complexities for operational applications. First is the challenge of Table 5.2 SSM Time-of-Flight at Long-Range Engagements Missile Time-of-Flight to Target (Minutes) Engagement Distance (nm) Missile Missile Type Speed (Mph) 100 200 400 600 Cruise 500 12.0 24.0 48.0 72.0 Cruise 600 10.0 20.0 40.0 60.0 Cruise 700 8.6 17.1 34.3 51.4 Supersonic 1,000 6.0 12.0 24.0 36.0 Hypersonic Mach 5 3,000 2.0 4.0 8.0 12.0 Hypersonic Mach 7 5,000 1.2 2.4 4.8 7.2

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hypersonic weapons that pose severe requirements for tracking that are required by the defender. Sensors must track the maneuvering boost-glide weapons through a plasma sheath and provide sustained high accuracy tracking to enable intercept. The time-to-intercept is very short and national efforts are required to develop intercept capabilities. The second complexity is the operational need for missile salvo analysis to determine the appropriate number of fires (a salvo) necessary to achieve target destruction. Typical operational analyses must consider at least the following factors to determine salvo sizes [15]: • Missile single-shot kill probability (SSPK or PKSS); • Effect of target deceptive measures (signature deception, deceptive maneuvering, decoys, etc.); • Effect of close-in weapon systems designed to disable the missile by cyber, electromagnetic, or cyber means. These operational factors introduce two key challenges that must be addressed by technical solutions: 1. Prioritize and orchestrate the net at critical times, particularly during critical tracking periods prior to and during engagement. The system must be able to project ahead and assure that sensors and network access will be available (especially space and unmanned platform sensors) during critical tracking and transition to engagement and missile flight stages. 2. Position and orchestrate distant sensors to achieve critical accuracy and ID during these periods. Unmanned sensor platforms must be positioned, and sensors pointed to provide the necessary data to support engagement. The solution to these requirements requires productive models that fastsimulate the engagement and orchestrate sensing and relay channels to assure engagement continuity of service. In conclusion, it is important to recognize that humanitarian organizations have examined the legal issues raised by remote attacks at long ranges employing capabilities such as cooperative engagement with advanced weapons. While not technical, the operational considerations of the principles of targeting law for long-range engagement must be developed (rules of engagement that consider criteria for distinction, discrimination, proportionality, and precaution) [16].



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5.4  Challenge 4: Integrating Cyber Capabilities While the prior three challenges are driven by the physical properties of time, space, and kinematic behaviors, the challenge of integrating cyber defense and cyber offensive capabilities poses a different kind of operational need. Offensive adversary cyber activities (e.g., probes, disruptions, access, and exfiltration) occur in seconds to milliseconds (or less) and the decision response must be in a similar time frame. The cyber and EW targets are nonphysical, seeking to attack information and ultimately decision making in the minds of humans (analysts, operators, and commanders). Of course, a primary target of the DMO fleet will be its networking capability; as the network degrades, so does distributed lethality, and the widely distributed vessels become more vulnerable as they must fall back to their organic sensors. The DMO fleet will be challenged by two categories of cyber threats: • Ad hoc and continuous threats are continual external cyber activities that probe, test, and attempt to disrupt the network through any point of access on communication links or computer nodes afloat or ashore. • Advanced persistent threats (APT) that “…possesses sophisticated levels of expertise and significant resources which allow it to create opportunities to achieve its objectives by using multiple attack vectors (e.g., cyber, physical, and deception). These objectives typically include establishing and extending footholds within the information technology infrastructure of the targeted organizations for purposes of exfiltrating information, undermining or impeding critical aspects of a mission, program, or organization; or positioning itself to carry out these objectives in the future. The advanced persistent threat: (i) pursues its objectives repeatedly over an extended period of time, (ii) adapts to defenders’ efforts to resist it, and (iii) is determined to maintain the level of interaction needed to execute its objectives” [17]. Will the fleet go to sea with a covert APT present? In spite of all the security processes in place, it must be prepared for just such an eventuality. Consider just a small set of the attack surfaces (categories of attack entry) and vectors (specific targets of access to the network) that must be protected by the fleet cyber team (Table 5.3). The threats are diverse (ranging from perinserted malware in the supply chain to real-time targeted service denial) and dynamic as attacks may change throughout an engagement period. The cyberattacks can be expected to be coordinated with attacks in other domains to stress the use of the network for ISR and C2 activities.

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Cyber Attack Surfaces Network Computing

Attack Vectors Employed by Adversaries Network probing and mapping Access leapfrog from open internet to secure

Description/Example Probe network to map the architecture and properties of any aspect of the fleet nets Exploit bridge vulnerability between networks, maintain an advanced persistent threat mechanism Denial of service (DoS) by Insert large numbers of targets electronically or overwhelming input physically (e.g., decoy small vessels, and UAVs) Malware insertion Insert malware via exploited access channel or (electronic) persistent Malware insertion (physical; Insert malware into commercial components supply chain) (hardware, firmware, software) in the supply chain Network Comm link disruption/denial Jam network links (e.g., low signal satellite links) Communications (jamming) Links Comm link denial (relay Attack or jam physical relays (e.g., satellite, UAS destruction) nodes) Comm link exploitation Analyze network traffic to exploit comm patterns (traffic analysis) NetworkNetwork personnel social Social engineer cyber administrator personnel to Related engineering inadvertently expose information Personnel Spear phishing attack on Broadcast dedicated spear phishing attacks to ashore admins seduce low-level net personnel to ingest malware Error-inducing attacks on net Induce an error in configuration by inserting personnel controlled instructions to net administration personnel False data insertion across Insert data to reduce network or ISR (user) network personnel confidence in the network and its application Physical Attack Confusion-inducing attacks Create critical network conditions (e.g., physical on net personnel node attack or electrical power disruption), induce network actions that expose the net to exploitation

While the fleet conducts cyber defense against such threats, it also conducts cyber ISR to explore the adversary cyber operating environment that poses threats [18]. The mechanisms of computer network exploitation (CNE) are employed to passively and actively engage adversary networks to understand the cyber operating environment. The concept of persistent engagement is where cyber interactions with adversaries are ongoing, at a level below armed conflict, but not simply responsive. It embraces forward defense where a persistent contest (detect, parry, counter-prob, etc.) is ongoing between cyber adversaries [19].



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Because of the ubiquitous presence of the cyber domain, ashore cyber analysts support this activity from afar as well as afloat analysts that are physically close-in to threats and may have greater access to aspects of adversary networks. Ashore-afloat coordination of cyber analysis and attack is critical to achieve an integrated response to all levels of an adversary organization structure. But cyber cannot be considered an independent domain—adversaries may compound cyber-attacks with assaults in other domains. Defensive countercyber operations refer to those activities that prepare for and respond to adversary attacks across all surfaces. For example, an adversary may coordinate network denial actions (jamming comm links) with a network computing disruption (denial of service (DoS)) to limit ISR the ability to observe a critical Red fleet maneuver. The adversary may further enhance the effect by coordination with a SIGINT deception that produces distracting decoy signals that will cause ISR assets to focus away from the critical Red maneuvering. Cyber awareness, in this case, must be fully coordinated with SIGINT and other sources to detect the actions, infer the denial/disruption and deception intentions, and respond with countermeasures.

Endnotes [1] Joint Publication (JP) 3-0, Joint Operations, describes the operational environment as encompassing the physical domains of air, land, maritime, and space, the IE, which includes the cyberspace domain, and the electromagnetic spectrum. It also describes the joint functions as related capabilities grouped to help commanders integrate, synchronize, and direct operations. The joint functions are C2, information, intelligence, fires, movement and maneuver, protection, and sustainment. [2] The U.S. DoD envisions a Joint All-Domain Command and Control (JADC2) architecture to enable commanders to (1) rapidly understand the battlespace, (2) direct forces faster than the enemy, and (3) deliver synchronized combat effects across all domains. See Hoehn, J. R., Joint All-Domain Command and Control (JADC2), Congressional Research Service, IF11493, updated July 1, 2021. [3] Air Force Doctrine Note 1-20, USAF Role in Joint All-Domain Operations. [4] Each service has a unique role and perspective on its own contribution to all-domain operations, for example, see the U.S. Air Force perspective in Air Force Doctrine Publication (AFDP) 3-99, Department of the Air Force Role in Joint All Domain Operations (JADO), October 8, 2020. [5] Galdorisi G., and R. Goshorn, “Maritime Domain Awareness: The Key to Maritime Security Operational Challenges and Technical Solutions,” in Proc. 11th ICCRTS, September 2006. [6] For reference, the South China Sea is a marginal sea that is part of the Pacific Ocean, encompassing an area from the Karimata and Malacca straits to the Strait of Taiwan of around 3,500,000 km2 (1,400,000 mi2). The China Sea Basin has a maximum depth of 5,016m.

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[7] In data fusion terminology, we define correlation as the process of measuring quantitatively the similarity between two measurements (e.g., same lace, same time, same observed attributes) and association as the decision process of assigning two or more measurements to a comm on target. Correlation precedes association, and association preceded combining attributes or fusion of data. [8] Hughes, W., and R. Girrier, Fleet Tactics and Naval Operations, Annapolis, MD: Naval Institute Press, 2018, pp. 18–19. [9] See Blackman, S., and R. Popoli, Design and Analysis of Modern Tracking Systems, Norwood, MA: Artech House, 1999. [10] This figure is adapted from the fundamental regions of tracking performance developed in the classic textbook on tracking. See Blackman, S. S., Multiple-Target Tracking with Radar Applications, Dedham, MA: Artech House, 1986, pp. 12–13. [11] The CEC and integrated fire control capabilities have been evaluated in experiments since the 1990s. The descriptions are based on the following relevant documents: Grant, C. J., “CEC: Sensor Netting with Integrated Fire Control,” Johns Hopkins APL Technical Digest, Vol. 23, No. 2 and 3, 2002, pp. 149–161. Young, B. W., “Future Integrated Fire Control,” Proc. 10th International Command and Control Research and Technology Symposium, June 2005. Deering, V., et al., “Open Architecture as an Enabler for FORCEnet,” NPSSE-06-002, Naval Postgraduate School, September 2006. [12] Young, B. W., “Future Integrated Fire Control,” Proc. 10th International Command and Control Research and Technology Symposium, June 2005. [13] “Russia says it successfully tested hypersonic missile praised by Putin,” Reuters, July 19, 2021, https://www.reuters.com/world/europe/russia-conducts-ship-based-hypersonicmissile-test-ifax-cites-defence-ministry-2021-07-19/. [14] LaGrone, S., “Unmanned Systems, Passive Sensors Help USS John Finn Bullseye Target with SM-6,” U.S. Naval Institute, April 26, 2021, https://news.usni.org/2021/04/26/ unmanned-systems-passive-sensors-help-uss-john-finn-bullseye-target-with-sm-6. [15] For a complete discussion of missile salvo and fleet exchange equations. See Hughes, W., and R. Girrier, Fleet Tactics and Naval Operations, Annapolis, MD: Naval Institute Press, 2018, Chapter 13. [16] Boothby, W., “Some Legal Challenges Posed by Remote Attack,” International Review of the Red Cross, Humanitarian Debate: Law, Policy, Action, New technologies and Warfare, Vol. 94, No. 886, 2012, pp. 575–595. [17] NIST. Managing Information Security Risk: Organization, Mission, and Information System View. SP 800-39 (2011). [18] Votipka, D., et al., “ISR and Cyberspace,” Air University, 6 July 2017, https://www. airuniversity.af.edu/CyberCollege/Portal/Article/Article/1238539/isr-and-cyberspace/. [19] Nakasone, P. M., “A Cyber Force for Persistent Operations,” Joint Forces Quarterly 92, 2019, pp. 10–14. See also Healey, J., and S. Caudill, “Success of Persistent Engagement in Cyberspace,” Strategic Studies Quarterly, Spring 2020, pp. 9–15.

6 Maritime MultiINT Fusion Processes In Chapter 4 we enumerated the many state-of-the-art systems applied across advanced navies to integrate organic and nonorganic sensors and sources, generally integrated by ISR mission (antisubmarine warfare, surface warfare, antiair warfare, etc.). In this chapter we introduce concepts and technologies to significantly extend the integration and automation to a greater degree beyond what we described in Chapter 4—including processes to estimate the entire maritime situation across the missions and the domains of space, air, surface, subsurface, and the seabed. The concepts of conducting ISR from seabed to space and performing joint all-domain operations anticipates a level of integration that provides “commanders access to information to allow for simultaneous and sequential operations using surprise and the rapid and continuous integration of capabilities across all domains—to try to gain physical and psychological advantages and influence and control over the operational environment” [1]. For example, the Air and Space Force perspective is that, “In JADO, intelligence must develop, maintain, and share an awareness of the operational environment that spans geographic, functional, domain, classification, and organizational boundaries. The scope of awareness should include intelligence on ongoing operations, adversary forces, indications, and warnings (I&W), target information, and account for military, political, and environmental considerations” [2]. The high level of integration of information requires technology to provide broad coverage, rapid access to collected information, and responsive control of the collection network. It is important to distinguish the terms that we use and explain the nuances in terminology regarding the integration of diverse sources of intelligence: 107

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Data fusion is a broad term that refers to the process of combining data or information to estimate or predict entity states. The terms multisensory and multisource data fusion, and information fusion all refer to technical processes that perform data fusion. The term “process” can refer to human activities (e.g., analysis) or to computational algorithms, but it is most applied to refer to the latter [3]. MultiINT refers to the automated processing of multiple shared sources of raw or preprocessed intelligence to derive integrated intelligence products. The study of MultiINT processes is an interdisciplinary field seeking to understand how integrating intelligence across the intelligence cycle can vastly improve tactical and strategic decision-making. This is achieved by using processes, algorithms, and systems to extract knowledge and insights from data that ranges from being abundant to being sparse and unstructured [4]. All-source intelligence analysis refers to the analytic process performed by human analysts with access to all available intelligence sources. This term was originally intended to distinguish all-source from single-source disciplines (e.g., SIGINT, GEOINT, HUMINT, and OSINT analysis) [5]. While intelligence in the maritime environment has always focused on naval vessels, especially combatants, the expansive definition of the maritime domain has expanded to include “all areas and things of, on, under, relating to, adjacent to, or bordering on a sea, ocean, or other navigable waterway, including all maritime-related activities, infrastructure, people, cargo, and vessels and other conveyances” [6]. Complete all-domain naval intelligence preparation of the operational environment and situation awareness should account for the range of maritime battlespace dimensions that include [7]: • Geographical features and meteorological and oceanographic characteristics; • Population demographics (ethnic groups, religious groups, age distribution, income groups, public health issues); • Political and socioeconomic factors (economic system, political factions); • Infrastructures, such as transportation and information systems; • Rules of engagement (ROE) or legal restrictions on military operations as specified in international treaties or agreements; • All friendly and adversary conventional, unconventional, and paramilitary forces and their general capabilities and strategic objectives;



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• Environmental conditions (earthquakes, volcanic activity, pollution, naturally occurring diseases); • Psychological characteristics of adversary decision-making; • All locations of foreign embassies, nongovernmental organizations, and private volunteer organizations. In this chapter, we focus on MultiINT fusion as an automated process to combine data across seabed-to-space sources to estimate the maritime threat situation in all physical domains of operations, as well as in cyber, geopolitical, and the other dimensions of the maritime domain. This chapter and the next address solutions to the four challenges posed in Chapter 5, explaining how automated and distributed data fusion enable distributed maritime operations.

6.1  The JDL Model for Organizing Naval ISR Fusion The fundamental framework for describing the process for integrating data from diverse sources is the JDL data fusion model. Developed in the early 1980s by a subpanel of the U.S. DoD Labs, the basic model has remained an organizing structure to define the data fusion process: …A process dealing with the association, correlation, and combination of data and information from single and multiple sources to achieve refined position and identity estimates, and complete and timely assessments of situations and threats, and their significance. The process is characterized by continuous refinements of its estimates and assessments, and the evaluation of the need for additional sources, or modification of the process itself, to achieve improved results [8].

This section provides a brief overview of the model related to naval ISR; established texts provide a through treatment of the mature model [9]. The most basic JDL model (Figure 1.6) represents the flow of analysis and, as an abstract model, can refer to a process performed by humans, machines, or both. It is similar to a fundamental model of human cognition that has the stages (or levels) of sensation, perception of objects, perception of situations of objects over time, and reasoning that construct meaning, followed by action. The Kahneman research in human cognition has distinguished between two modes, or systems of thought: fast (intuitive, emotional) reaction, and slow (deliberative and logical analysis); these feedback loops to response are indicated by the (1) and (2) arrows in the model, respectively [10]. The fast loop includes immediate actions in response to a detection (e.g., new vessel appears in threatening position) and reactive tasking to identify the vessel. The full JDL loop includes the

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complete deliberative reasoning through all levels of the process. The SHOR (sense, hypothesize, orient, respond), OODA, TCPED (task, collect, process, exploit, disseminate), and other C2 models are similar efforts to represent the fundamental elements of observing, thinking, and response. The benefit of the JDL model is that it provides an organizing scheme for distinguishing the stages or levels of abstraction at which cognition, analysis, or automated fusion is performed. The levels of processing (or, cognition) are distinguishable, each with a definable role in moving from raw sensation to perception, comprehension, meaning (relative to a mission goals), and intuitive reaction and reasoned response. From the basic model we can organize a more detailed naval ISR model (Figure 6.1) that distinguishes the four most basic JDL levels of processing in the maritime context [11]. Level 0. In some cases, it may be possible to combine raw data from multiple sensors prior to the detection in each individual sensor. This upstream fusion can be particularly valuable when the signals in both sensors are weak and even undetectable independently. The process minimizes information loss that results from the detection process in each sensor that may reject weak signals; the combination of correlated weak signals across both sensors may result in a confident detection [12]. The process occurs upstream, and sensors must pass the predetection data to the level 0 fusion processor. Level 1. Object refinement performs the detection of objects, and refines the estimate of state (location, identity, track behavior, etc.) and maintains custody of the object over time as sensing data allows. Sensor data must be correlated (measure the degree of similarity of two observations) and associated (decision to assign observations across sensors to a single object). Once associated, the sensor data is used to refine the state and then predict the next state at the next collection opportunity. In the maritime case, for example, a ship’s radar track may be correlated, then associated with a commercial satellite’s AIS track of the ship to identify the vessel. The specific processes performed here include: • Alignment: First, the data from different sources must be aligned with a common time and space reference system (as well as common scale of confidence, probability, and other measurements). • Correlation: Observations must be compared by some measure of similarity or closeness in time, space, or attributes. • Association: Once the correlation measurement between observations meets a likelihood criterion for similarity, the observations may make an association decision to assign the observations to the same target object. • Tracking: The state of sensor observations over time may be computationally modeled to create a track—a state model of the object initiated

Maritime MultiINT Fusion Processes

Figure 6.1  The JDL model as a closed-loop sensor control system.

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and updated over time. The track model may be used to predict the future position of an object for comparison (correlation and association) with a future observation. • Identification: Identification is the process to determine the type of a target (e.g., a surface vessel), the classification (e.g., a merchant ship or a guided missile destroyer model 55), and identity (e.g., hull number 047). Level 2. Situation assessment considers the relations among objects (in time, space, and behavior) to assess the meaning of any relations; this is a higher level of abstraction and considers the events, activities, context, and timing factors to derive a situation estimate. In the maritime case, this will assess the aggregate behavior of adversary combatant vessels, their time-from-port, fuel, and readiness status, coordinated behavior (maneuver, active sensors, communications, etc.), predicted mission, and intent. Even higher-level context must be considered, including geopolitical context, weather and sea state, and windows of commercial satellite coverage [13]. Level 3. Impact assessment is where the estimated situation is compared to the fleet mission and the effects of the current situation (e.g., Blue fleet and Red fleet) are estimated. The potential vulnerabilities of Blue (e.g., exposure to Red sensors and weapons) as well and the opportunities for Blue (e.g., covert, undetected sensing of Red) are projected for the commander [14]. Level 4. Process refinement performs the ISR resource management to orchestrate the network of sensors to close intelligence gaps and focus tracking to maintain custody on major threats (or to resolve key unknowns). This is a feedback level, adapting collection by coordinating all ISR sensors to optimize a sensing objective function (maximum custody of key targets, maximum search coverage, maximum track accuracy on key threats, etc.). While this feedback is essential for an adaptive sensing system on a single platform, it is also essential for a distributed system where distributed operations require control of widely distributed sensors (seabed to space). In a distributed environment level 4 is required to: (1) orchestrate across all sensors to assure coverage (for search and surveillance) and focus (for targets that must remain in custody), and (2) adapt the computation and network communication to maintain processing and bandwidth allocations based on the situation. The JDL model figure only represents the abstract levels of a single fusion node that combines multiple sources, but we must recognize that the distributed naval fleet has many interconnected fusion nodes that perform different levels of the fusion process on different target types (air, surface, and subsurface). Each node is unique to its mission and its access to sources; and it may be reporting fused information to other fusion nodes to contribute their fusion



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process. To avoid over-reporting (double-counting), the fused information must maintain a degree of metadata to enable appropriate fusion across the network: • Data pedigree is the description of data source and attributes of collection and fusion process (generally maintained in metadata that uniquely defines the data itself and provides a traceable path to its original collection). • Data provenance is a broader term that often includes pedigree as well as the process (or network of fusion nodes) by which it arrived in an intelligence or fire control database, and processing performed along the way. Aspects of provenance also include data quality and attribution (source, sensor, platform, classification, etc.) Some degree of provenance can prevent double-counting and over-processing combined information. Figure 6.2 is a basic example to illustrate just one possible fusion architecture that allocates processing at different JDL levels across different naval platforms. Note that we only included one of each type of platform to simplify; a fleet architecture will include scores of platforms and hundreds of potential fusion nodes. The figure points out three implementation issues that are important for distributed naval implementations: • Central fusion: Major nodes will perform all levels of fusion and distribute results (common operating picture) to other nodes. It is critical to retain pedigree of data and fused results to avoid multiple counting of objects, events, or activities. • Distributed fusion process distribution: Fusion processes may occur at all nodes in the naval network (sensor nodes, relay nodes, and C2 nodes). Even a sensor node, for example, may receive data from other sensors (e.g., tips to focus on a target) and may perform some form of fusion for its own functions (e.g., to evade threats or to adapt processing and sensing). We discuss this further in Chapter 7. • All-domain fusion: While the JDL model was initially applied to physical objects (ships, submarines, and maritime aircraft) it has also been applied to represent the processing of sensor and source data on nonphysical objects of interest to naval intelligence: signal contents, cyber-attack processes, social media messages, social attitudes of ashore populations, and networks of adversarial naval organizations. Naval ISR must consider the integration of all these domains to assess the overall situation and understand a coordinate multidomain activity.

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Figure 6.2  JDL structure for elements of a maritime fusion network.

In the next sections we expand on the operations performed at each of the levels in the maritime context.

6.2  Maritime Object and Situation Assessment Levels 1 and 2 The result of an automated level 1 process (also called object-based production (OBP)) is a dynamic data base that contains all objects in a theater of operations: objects, unique identifiers, their identities if established, the dynamic tracks if established, links to prior activities of an identified object (port calls, patterns of behavior, etc.), basic data on the object (e.g., basic ship characteristics and capabilities), and additional assigned data (e.g., the object relative priority). We illustrate the implementation of a representative level 1, 2, and 4 process for a maritime situation assessment mission in Figure 6.3 where a sequence of commercial satellite sensor observations (e.g., SAR and EO, as described in



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Figure 6.3  Maritime situation assessment and collection planning process.

Chapter 9) are collected and processed to derive a maritime situation. In addition, we may have access to commercial AIS data that reports the identity and location of vessels to compare with the EO and IR imagery detections. We adopt the key terms (for a thorough overview see [13]): • Activity refers to a recognizable movement, conducted by a single entity that is an indicator or has a specific meaning when viewed within a relevant context [15]; • Situation refers to a set of environmental elements and events or activities with respect to time or space [16]; • Situation detection refers to the process of perceiving, identifying, and declaring specified situations; • Situation awareness refers to the process of perceiving the environmental elements and events or activities with respect to time or space, the comprehension of their meaning, and the projection of their future status. (Inferred from [16].) This maritime implementation is a model-based approach to fusion because it applies reasoning that compares sensor and source evidence to known threat models (e.g., ship object models, ship and aircraft behavior models, and ship and aircraft electronic signature models). The process attempts to instantiate models as target hypotheses that can then be developed by seeking additional supporting or refuting evidence for the hypothesis, by requesting additional ISR collection. The model-based fusion and data-driven collection approaches were recommended by a joint DoD-Intelligence Community Science Board for these kinds of ISR challenges [17]. The automated process proceeds in the sequence that follows (Figure 6.3):

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1. Level 0: Detect objects upstream. In some cases, it may be possible to combine raw data from multiple sensors prior to the detection in each individual sensor. This upstream fusion can be particularly valuable when the signals in both sensors are weak and even undetectable independently. The process minimizes information loss that results from the detection process in each sensor that may reject weak signals; the combination of correlated weak signals across both sensors may result in a confident detection [12]. The process occurs upstream, and sensors must pass the predetection data to the fusion processor. In this case, the raw EO and SAR data would have to be processed together to detect weak image targets. 2. Level 1: Detect and refine maritime objects. Radar and EO imagery are processed to detect and extract objects (vessels) and their locations (all imagery is first orthorectified to remove geometric distortions and reference to map coordinates). At this stage the objects may be correlated to AIS transponder reports to identify those vessels reporting their identity. The extracted radar and EO image chips (small images that just contain the vessel object) are passed to a target classifier (e.g., a neural network classifier) to determine the object class (merchant, naval combatant, fishing) and type (e.g., cruiser, destroyer, frigate). This process is referred to as OBP where POSIT objects are extracted and placed in an object store for: (1) subsequent tracking processes, (2) tipping to cue up other sensors (or cross-cueing) to identify an object, and (3) development of the pattern of life of the objects or clusters of objects. OBP is the process of organizing intelligence around objects of interest as a means of organizing information for analysis. OBP methods provide digital intelligence reports to reflect all of the information on objects (real-world things and events) and to deliver assembled reports that include all relevant information [18]. All observations are managed (indexed, described, and organized) in a common structure and are based on the object observed—this process is called structured observation management and allows MultiINT sharing across organizations [19]. 3. Level 2, Step 1: Detect activities. Activities are characterized by objects (vessels) moving and interacting with other objects and significant locations. Activities obviously occur over a specified period of time. Thus, detecting an activity involves looking for correlations in the relationship of these objects and locations in space and time. Several classes of algorithms can be applied to perform the detection with this data, ranging from track template matching to learned neural nets. The relative effectiveness of these techniques will depend on the qual-



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ity and quantity of examples available for the activities to be detected. Some machine learning techniques can detect coordinated actions that may indicate an activity that does not match a previously learned and labeled pattern. Research has demonstrated the performance of processes to detect and identify ship tracks based on the correlation of AIS tracks, satellite SAR ship detections, and medium spatial resolution satellite imagery [20]. 4. Level 2, Step 2: Assess situation. Once an activity or set of activities are recognized, the time series of activities, and any correlation between their activities and contextual factors (weather, tactical, etc.) are used to assess the meaning of the situation. This assessment can be in the form of a recognized tactical maneuver, a maritime activity (e.g., at-sea refueling), a projected future (loitering in preparation for convoy), or other maritime situations. The importance of the activity can then be assessed. More importantly for our purposes, the degree of certainty about the situation and the projection of the situation at future times can be evaluated. This phase can include deeper automated and human-machine analysis using additional contextual data. For example, one situation assessment process detected suspicious vessel loitering behaviors to the graph network of shipping companies, their addresses, owners, and the countries in which they are flagged. The methods sought to identify gray (nontransparent) shipping networks that could support state-directed hybrid maritime warfare [21]. The additional context of the shipping ownership helped score the most likely enablers of hybrid activities. The networks could be expanded to include historical behaviors (e.g., suspicious loitering, commercial vessels cotraveling with military vessels, or being fitted at military shipyards) of vessels. If the development of the future situation is uncertain, but it can be asserted that specific data elements could resolve or reduce the uncertainty, then a collection task may be created to address the uncertainty. 5. Level 4: Plan/schedule next collection. If collection of certain data can disambiguate a developing situation, then there are two basic questions that need to be answered in order to plan the appropriate tasking actions by available sensors. The first question is “What objects or activities need to be ascertained, and what sensors can supply that information?” The second question is, “Over what future time horizon is this data likely to be available or useful and when is it needed to impact the situation?” Additionally, we must consider the relative value of the many targets we wish to observe. (By value, we mean a measure of mission importance. Often value is used to describe a candidate col-

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lection and may depend on target importance as well as type of sensor data to be collected, time, and location.) Once these questions are answered we are left with a straightforward (though computationally difficult) optimization problem. The scheduling process must consider a large number of alternative collection options and sequences, then choose among alternatives, based on maximizing a value-based objective function. (We discuss this further in Section 6.4.) In some cases, trained collection managers can perform this function, but for many cases (such as satellite collection) the speed and number of options to be considered dictate the need for automation. For modest numbers of sensors over a short timeframe, techniques such as mixed-integer linear programming are appropriate. For very large numbers of sensor opportunities and/or targets, more powerful techniques are required. Furthermore, it is likely that a truly optimum solution will not be obtainable in time to task sensors and that a satisfactory solution must be sought instead. The preceding example illustrated the processes for levels 0, 1, and 2 for the traditional naval maritime domain awareness mission, focusing on physical objects of military interest. It is necessary to point out that the fusion process is also applied to discover, understand, and model the dynamics of entities that are nonphysical (social networks, populations, economies, etc.). Nonphysical entities are the objects (targets) of information operations and special operations focused on organizations whose dynamics do not follow the kinematic behaviors of the physical world. Navies have ISR tasks that require monitoring social influence in ashore populations, civilian ashore social networks, criminal human networks, adversary command organizations and their human decisionmaking, and more. Activity based intelligence (ABI) is a MultiINT discipline characterized by analysis of human activity and transactional data to resolve unknowns using contextual knowledge of activities and transactions. The analytic focus is on entities (people and their organizations), their relationships (networks of entities), their activities, and patterns of behavior. ABI is contrasted with traditional target-based intelligence because of its focus on discovering the relationships and behavior (activities) of networks (target systems) [22]. The contrast in the JDL levels between the physical and nonphysical target systems (Figure 6.4) shows the mapping between JDL target-based fusion notions and nonphysical networks that are represented as graphs where objects are referred to as entities, groups or units are referred to as networks or systems, and target tracking is more generally referred to as behavior (kinematic tracking is replaced by social dynamics or network dynamics and modeled appropriately). The general term for a generic person, place, or thing is object, as we



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Figure 6.4  Applying the JDL fusion model to nonphysical systems.

have used throughout. A specific named or labeled object (e.g., John A., Farris Building, homemade explosive) becomes an entity, in some parlance. The steps in Figure 6.4 applied to ISR of foreign ashore terrorist networks resolves the individual human objects (generally called actors because they are socially interacting humans) at level 1, then discovers the relationships (communications, financial, influence, etc.) between actors to form networks at level 2. The structure can be studied by social network analysis (SNA) to derive the importance and influence of the individual actors in the net; the timeline of events of the network helps to explore behavior, patterns of life, and potential future courses of action at level 3. Unique to complex human systems, the feedback (level 4) actions not only include sensor management to refine the network, but the use of probing actions to influence the complex adaptive network to reveal information. Probes include any action designed to produce a response that reveals new properties of the network. (The analogy in dynamic systems is the injection of an impulse to measure the output impulse response function that reveals properties of the system.) For example, the analyst refining a terrorist network that is messaging on social media may send a set of messages designed to elicit a response to determine how (and who in the network) responds to a meme or concept. Example sources and analytic targets of nonphysical (or human) systems of interest in the maritime domain include: • Fishing fleets. Source: radio traffic in fishing fleet. Analytic targets: radio network, legitimate and illegitimate activities, communicating parties;

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• Ashore terrorist organizations. Sources: social media, ashore HUMINT. Analytic targets: terrorist groups, actors, major influences, target audiences of public messaging; • Foreign naval units in area. Sources: radio traffic, ashore naval intelligence, public port departure data. Analytic targets: crew, organizational network across the fleet; • Ashore Ports (prearrival). Sources: public broadcast, news, social media, PAI, HUMINT. Analytic targets: threat and criminal organizations and individuals, threat activities.

6.3  Maritime Impact or Threat Assessment Level 3 The definition of impact assessment introduced earlier in this chapter addressed the need to identify the effect of the current (and projected) situations on the vessel, force package, or fleet’s mission [13]. This effect, or mission impact, includes both opportunities and threats. In the first chapter, we introduced the wide range of naval domains (Section 1.1), missions (Section 1.2), and operations (Section 1.2); in each there are unique contexts, dispositions, spatial geometries, activities, and events that indicate opportunities to act, or threats to avoid, deter, or defend. The distinctions between threats and opportunities in the maritime context are illustrated for the maritime superiority mission (Table 6.1) with specific examples of the kinds of situations that naval officers are trained to recognize, and that automated Level 3 fusion processes seek to recognize and adapt sensors to observe. These kinds of situations include, for example: • Maneuvering to certain relative geometries of vessels or aircraft (manned and unmanned); • Positioning of vessels or aircraft to critical distance between vessels relative to weapon ranges; • Timing of events and sequences of activities; • Coordinated behaviors of assets (subsurface, surface, air, and overhead surveillance) including loitering, merging, co-traveling, maneuvering to firing positions or protective positions, deceptive behaviors, massing of forces, and more; • Electromagnetic activities such as going dark with EMCON, illumination of vessels with fire control sensors (e.g., high pulse repetition frequency radar), coordinated electronic activities, jamming, spoofing or other deceptive actions, and so on.



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Table 6.1 Maritime Superiority Opportunities and Threats Area Objective

Example T/O Behaviors

Maritime Opportunity (O) Offense: Degrade, disrupt, deceive, deny, and exploit enemy’s IE to introduce fog; mass distributed forces decisively engage the enemy’s critical vulnerabilities to degrade and remove the enemy’s center of gravity. Opportunity situations for Blue: ·· Red vessel or aircraft position, electronic state, sensor positioning, communication ability, or other condition degrades its situation awareness; ·· Red sensor gap enables a deceptive maneuver or EM action; ·· Red network degradation, even if temporary; ·· Red vessel positions are such that a sensor ambush can be induced by Blue actions; ·· Red forces are dispersed to a high degree of vulnerability to electronic attack to disrupt networking.

Maritime Threat (T) Defense: Protect, defend, recover own IE, and increase the enemy’s friction by creating uncertainty.

Threatening situations to Blue: ·· Red vessels have successfully maneuvered (electronic, cyber, physical) such that Blue loses custody; ·· Red forces (surface, air) are massing and maneuvering in the direction of Blue forces; ·· Red platforms are surveilling distributed Blue forces to the degree they have near-full custody of Blue; ·· Red raises intensity of offensive cyber operations coordinated with or preceding threatening physical behaviors; ·· Red forces exhibit coordinated denial (e.g., go AIS dark) and deceptive (e.g., AIS spoofing) behaviors.

The role of level 3 is critical to provide warning to C2 officers and systems of situations that require attention and action. Level 3 simulations that explore future courses of action may be integrated with C2 systems to allow operations planners to explore future courses of action and potential outcomes. The current situation simulation (provided by the ISR model) can be extended forward in time applying alternative Red and Blue COAs to examine potential effects and outcomes [23]. In Chapter 8, we introduce further potential contributions from AI technologies to aid decision-makers with broader context, situation recognition, and COA simulation [24]. Automated level 3 processes implement in a computational form, the maritime version of the Joint Intelligence Preparation of the Operational Environment (JIPOE) process described in DoD publication JP 2-10.3 [25]. JIPOE is the analytical process used by joint intelligence organizations to produce intelligence assessments, estimates, and plans in support of the joint force commander’s decision-making process. JIPOE is a continuous process that includes defining the current operational environment, evaluating the adversary, and determining and describing the adversary’s potential COAs to identify opportunities and threats. The four-step analytic process (1) describes the operating environment and the kinds of effects that are relevant to a commander’s mission, (2) describes the impact of the operational environment from a comprehensive

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systems-level viewpoint, including friendly and adversary forces as well as geospatial considerations, (3) evaluates the adversary in the context of the environment, and then (4) anticipates the likely and threatening COAs the adversary may take (Table 6.2).

6.4  Maritime Distributed Resource Allocation and Orchestration The allocation of maritime ISR sensors is performed at several levels of consideration (Table 6.3), beginning with a strategic assessment across the fleets and action groups, then at the operational fleet level to assign organic platforms Table 6.2 Computational Steps in the Level 3 JIPOE Process Representative Level 3 Fusion Computational Modeling Activities (with JP JIPOE Step 2-10.3 References) Step 1: 1. Model the commander’s intent for the JIPOE effort (JP2-10.3; II-4). Define the 2. Identify and model key effects relevant to the commander’s intent and the Operational systems within the environment that must be represented. Environment 3. Maintain a model library of the environment (JP2-10.3; II-5); identify the appropriate models and data required to instantiate component models and meta model for the environment (geospatial, weather, naval behavior, etc.). 4. Compose a baseline model, calibrate and validate behavior compared to prior months as appropriate for the environment and current situation. Step 2: 1. Update by ISR the current situation in the operational environment (JP2-10.3; Describe the II-11e Information Domain; II-12 Systems Perspective). For human social cultural Effects of the behavior issues, evaluate policy leaders, political parties, key population groups, Operational spoilers (e.g., terrorist groups), and their relations, positions, and relative politicalEnvironment social power. For the operational context, assess economics, corruption, security and terrorist trends, and media influences. 2. Accept and update information from current intel systems to establish model initial conditions and parameters. 3. Consider future outlook and impact of Op environment on adversary (Red) and Blue COAs. Using baseline simulation, project effects if no Red actions are taken and explain the dynamics of the environment (JP2-10.3; II-13 Systems Perspective). Step 3: 1. Review the modeled information about the adversaries and their relationships Evaluate the with other groups (JP2-10.3; II-14-16); identify uncertainties and sensitivities. Adversary 2. Identify sources of power and relative effects of alternative positions (JP2-10.3; II-17). 3. Identify relevant centers of gravity (JP2-10.3; II-18) across the Red and Blue systems. Step 4: 1. Perform analytic simulations to identify potential adversary objectives and Determine endstates (JP2-10.3; II-19-20). Adversary 2. Perform a range of exploratory simulations to evaluate potential effects of COAs adversary activities (e.g., maritime expeditionary campaigns, all-domain coordinated campaigns, political action, and media campaigns) to achieve their desired Red objectives and endstates (JP2-10.3; II-21). 3. Develop and evaluate the effectiveness of alternative adversary COAs (e.g., media campaigns, adverse influence-economic actions, maritime piracy, and maritime expeditionary campaigns).



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Table 6.3 Levels of Sensor Systems Allocation Level 1. Strategic ISR force management. 2. Operational force ISR allocation. 3. Tactical dynamic ISR sensor management.

Description and References Allocation of the ISR platforms across fleets to prioritize coverage of multiple operating areas [26]. Allocation of ISR platforms to multiple AOIs within a fleet [27]. Dynamic allocation of sensors-to-targets on individual ISR platforms in near real-time in a networked sensor environment [28].

across areas of interest (AOIs). Each allocation is based on an analytic process that must place a value on maritime activities in each area, and optimally allocate sensors platforms to activities. In this section, we consider the real-time dynamic allocation of sensors to search areas, surveillance areas, targets, target tracks, and target groups in the fusion level 4 process. The fourth fusion level manages the sensing capability of the system; in a maritime system, we refer to the network of sensors available to the fleet ranging from the seabed to space. In an ideal architecture, a network to all sensing resources could be accessible to the fleet and fully controllable to optimally sense the maritime environment, focus on threats, and adaptively manage the sensor platform or network to concurrently perform search, reconnaissance, surveillance, identification (e.g., dwell a sensor on an individual target, or interrogate a target for identification), and tracking to support engagement. The management of the network to coordinate all sensors as an adaptive sensing network is referred to as sensor or collection orchestration, and as a conductor harmonizes a multitude of instruments to produce harmony, this process seeks to optimize a multitude of sensors to produce feasible assignments and an optimum collection schedule. We consider two categories of required coordination control to maintain custody of many maritime targets for the dispersed DMO fleet (Table 6.4) [29]. • Sensor or collection orchestration is the structure of applications required to manage the sensor collection and network paths over time to proceed from search, detection, track-ID, and fire control. This fusion level 4 process requires the automation of the distributed network of MultiINT collection and processing elements to schedule dynamic collection on emergent events as well as preplanned (standing) collection needs in an optimal way. • Network orchestration: The network must adapt to vary coverage, focus, and priority as the system changes the importance of different missions (ocean search, ship-track, reacquire, and engagement), areas, and targets. The network must allocate bandwidth as phases of the mission change

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Sensor Orchestration MultiINT Sensor Planning Based on Target Value Target Sensor Aggregate Value Valuation Collection Optimization Feasibility Prediction ·· Coordination of multiple sensors to sustain continuity of surface, subsurface, or air target custody; ·· Prediction and valuation of each feasible sensor collection; ·· Assignment of values for priority targets-in-track.

Network Orchestration Route Planning Based on Message Priority Network Routing Message Priority Based Prediction on Current Projected ISRT State ·· Network instantaneous performance management priority message stream assurance (e.g., weapon inflight; target in-track).

over time. Of course, engagement operations must have priority during fire control activities that require sensors to provide frequent updates throughout missile flight. Table 6.4 illustrates the specific functions in each category of orchestration; both categories must be coordinated to operate the network and sensors to optimize an objective function. Then objective function must consider the balance between several alternatives: 1. Objective 1: Maintain custody of the maximum number of targets identified by the fleet as important and within DMO operating areas. 2. Objective 2: Maintain custody to achieve the highest aggregate target value, where each target has a defined value (e.g., an aircraft carrier has a higher value than a frigate). 3. Objective 3: Search an area to locate and acquire a threat vessel by maximizing the greatest coverage area in the shortest time. 4. Objective 4: Maintain network flow to minimize latency-inducing congestion, particularly during high-dynamic periods (the heat of naval battle). When optimizing sensor orchestration for a value there are two contributors to the value of a candidate target (or area) for collection. • Target value is the importance of a target, object, or activity (or an area when searching) that requires collection. High importance targets have a higher value. This is related to the intrinsic intelligence value of the knowledge about that target.



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• Opportunity value is the expected value of the collection on the target under predicted collection conditions (range, SNR, etc.). Of course, poorly observed collections (due to weather, obscuration, long range, etc.) have lower values. The orchestration process then applies an algorithm to assign an overall value (the collection value) to each collection request item (e.g., maritime target, or ocean search area) based on the combination of its target and opportunity values. The expected value must be based on a quantitative representation of the commander’s intelligence requirements (CIRs). The overall values of all targets (at a point or window of time) can be rankordered and optimized to achieve the highest overall value. Several approaches to optimization may be applied to perform the optimization, including: • Linear programming maximizes (or minimizes) a linear objective function subject to one or more constraints, where all the constraints are linear, and all of the search variables are continuous variables. These constraints are generally too restrictive for sensor assignment problems. • Mixed-integer programming adds one additional condition that at least one of the variables can only take on integer values. This method is most applicable to many assignment problems that are typical of sensor collection and resource allocation. • Simulated annealing is a probabilistic technique for approximating the global optimum of a nonlinear objective function. • Genetic search methods that apply heuristics to search large spaces (objective functions) either constrained or unconstrained, by expanding a search, choosing the best results, pruning unpromising results, and repeating the search for a global optimum. The purpose of the optimization is to use the collection resources available most efficiently and to dynamically focus attention on the most critical maritime targets. The effect is a dynamic control of the network and collection resources. But a widely distributed network of sensors is far from ideal and must address several challenges: • Control: Sensors vary widely in their ability to control their sensing modes, pointing, and access times. In addition, some sensors operate on platforms (spacecraft, aircraft, ships, or submersibles) that require the coordination of platform steering to position the sensor to have access (generally line-of-sight) to an area of target. Other sensors are fixed

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(seabed sensors) or floating (surface wave-glider sensors) or governed by regular physical laws (Kepler’s laws for spacecraft orbits) and only revisit areas or targets on a schedule. • Sensor coverage and target access: The field of regard, instantaneous field of view, and sensor platform motion determine the feasibility of a given sensor having access to observe a target, and the duration of the observation (the sensor dwell on a target). Coordination of sensors to observe multiple targets requires the ability to predict the feasibility of each sensor to have access to each target. • Network latency: The delays in networks limit the speed-of-control of sensors as well as the speed-of-response in sensor reporting. This delay can limit the performance of a networked sensor as the latency introduces a phase delay in closed-loop response of the sensor; this can severely limit the sensor’s ability to track a ship, for example. Furthermore, the delays from sensing to fusion across such a wide range of sensors can diminish the ability to perform some joint sensing modes (e.g., identification of a target by the features provided by two sensors at a coordinated time). This is not a large factor in single platform fusion, such as fusing the SAR, EO, and RF sensors on a maritime patrol aircraft, but it is a significant factor on widely distributed sensors.

6.5  Conclusion The subject of distributed sensor management is complex and the scholarly materials on algorithms for determining feasible solutions, selecting objective functions, and applying optimization methods is vast. Greater depth in this subject is beyond the scope and level of this book, but we recommend the following keys sources that address this challenge from different perspectives: • Hintz, K. J., Sensor Management in ISR, Norwood, MA: Artech House, 2020. • Hero, A. O., and D. Cochran, “Sensor Management: Past, Present, and Future,” IEEE Sensors Journal, Vol. 11, No. 12, December 2011. • Hero, A. O., et al., Foundations and Applications of Sensor Management, Springer, 2007. • Johnson, B. W., and J. M. Green, “Naval Network-Centric Sensor Resource Management,” Naval Postgraduate School, April 2002. • Ng, G. W., and K. H. Ng, “Sensor Management—What, Why And How,” Information Fusion, Vol. 1, No. 2, 2000, pp. 67–75.



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• For application to a maritime surveillance problem involving the allocation of multiple heterogeneous assets over a large area of responsibility to detect multiple drug smugglers, see Zhang, L., “Context-Aware Dynamic Asset Allocation for Maritime Surveillance Operations,” Journal of Advances in Information Fusion, Vol. 15, No. 1, June 2020. This chapter introduced the key functions, as if the fusion functions are performed in a centralized process; this may indeed be true on some platforms. But across the DMO fleet, there may be many fusion processors that share data and products across the fleet network. This network sharing and network fusion poses new changes. In the next chapter, we move to describe approaches to implement these fusion functions across networks where the fusion functionality is distributed and shared.

Endnotes [1] Congressional Research Service, Joint All-Domain Command and Control (JADC2), IF11493, Version 16, January 21, 2022. [2] Air Force Doctrine Publication 3-99 and Space Force Doctrine Publication 3-99, Department of the Air Force Role in Joint All-Domain Operations, November 19, 2021, p. 13. [3] Steinberg, A. N., C. L. Bowman, and F. E., White, “Revisions to the JDL Data Fusion Model,” Proc. Third NATO/IRIS Conference, Quebec City, Canada, 1998. [4] Center for MultiINT Studies, Naval Postgraduate School, https://nps.edu/web/cmis. [5] Fingar, T., “A Guide to All-Source Analysis,” The Intelligencer, Journal of U.S. Intelligence Studies, Vol. 19, No. 1, Winter/Spring 2012. [6] IMO definitions. See definition in “Executive Order on Promoting American Seafood Competitiveness and Economic Growth,” White House, May 7, 2020, https://www. wpcouncil.org/wp-content/uploads/2021/05/Executive-Order-on-Promoting-AmericanSeafood-Competitiveness-and-Economic-Growth-_-The-White-House.pdf. [7] Joint Staff, Joint Tactics, Techniques, and Procedures for Joint Intelligence Preparation of the Battlespace, Joint Publication 2-01.3, May 2020, pp. 24, II-4.5. Earlier doctrine in the period from 2003 to 2020 included distinct domains (air, land, maritime, and space), the IE (which includes cyberspace), and political, military, economic, social, information, and infrastructure (PMESII) systems and subsystems. Joint Staff, Joint Intelligence Preparation of the Operational Environment Joint Publication 2-01. 3, May 21, 2014, p. 1-1. For a critique of the PMESII elements, see Ducote, B. M., “Challenging the Application of PMESII-PT in a Complex Environment,” Fort Leavenworth, Kansas: School of Advanced Military Studies United States Army Command and General Staff College, April 26, 2010. [8] White, F. E., Jr., Data Fusion Lexicon, Joint Directors of Laboratories, Technical Panel for C3, Data Fusion Sub-Panel, Naval Ocean Systems Center, San Diego, 1987. See also

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[9] The following texts provide more depth on the data fusion model and process:



Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition, M. Liggins, II, D. Hall, and J. Llinas (eds.), Electrical Engineering & Applied Signal Processing Series, CRC Press, 2008. Hall, D., and S. McMullen, Mathematical Techniques in Multisensor Fusion, Norwood, MA: Artech House, 2004. Antony, R., Principles of Data Fusion Automation, Norwood, MA: Artech House, 1995. Waltz, E., and J. Llinas, Multisensor Data Fusion, Norwood, MA: Artech House, 1990.

[10] We introduced this comparison earlier in Section 1.4. See Kahneman, D., Thinking, Fast and Slow, Farrar, Straus and Giroux, 2011. [11] In this brief introduction we include the four basic levels. Level 0 was added for predetection fusion, or fusion at level of raw signals across multiple sensors. For additional changes and considerations, see [3] and Chapter 3 in Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition, M. Liggins, II, D. Hall, and J. Llinas (eds.), Electrical Engineering & Applied Signal Processing Series, CRC Press, 2008. [12] Newman, A. J., and G. E. Mitzel, “Upstream Data Fusion: History, Technical Overview, and Applications to Critical Challenges,” Johns Hopkins APL Technical Digest, Vol. 31, No. 3, 2013, pp. 215–233. [13] Alan N. Steinberg, “Foundations of Situation and Threat Assessment,” in: Handbook of Multisensor Data Fusion: Theory and Practice, Second Ed., M. Liggins II, D. Hall, and James Llinas (eds), Chapter 18, 2009. [14] For an example of a Level 3 implementation for a complex land scenario, see Chen, G., et al., “Game Theoretic Approach to Threat Prediction and Situation Awareness,” Journal of Advances in Information Fusion, Vol. 2, No. 1, June 2007, pp. 35–48. [15] GEOINT CONOP 2022, National Geospatial Intelligence Agency, April 1, 2016. [16] Endsley, M. R., “Toward a Theory of Situation Awareness in Dynamic Systems,” Human Factors Journal 37 (1): 32–64. [17] Report of the Joint Defense Science Board-Intelligence Science Board Task Force on Integrating Sensor-Collected Intelligence Office of the Under Secretary of Defense for Acquisition, Technology, and Logistics, November 2008, pp. 49–53. [18] Johnston, C., “Modernizing Defense Intelligence: Object Based Production and Activity Based Intelligence,” Briefing, June 27, 2013. [19] The term structured observation (or systemic observation) is a method of collecting and recording data used in numerous scientific disciplines. The structured procedures guide the method of observation and encoding of the observations for subsequent study. The terminology is adopted to refer to methods of structuring information from geospatial analysis for entry into object-based production systems. [20] See the example that uses AIS and satellite imagery: Štepec, D., T. Martincic, and D. Skoaj, “Automated System for Ship Detection from Medium Resolution Satellite Optical Imagery,” Proc. IEEE Oceans 2019, October 1, 2019.



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[21] Callaghan, C., R. Schroeder, and W. Porter, “Mapping Gray Maritime Networks for Hybrid Warfare,” COMSEC, July 1, 2020, https://cimsec.org/mapping-gray-maritimenetworks-for-hybrid-warfare/. [22] Biltgen, P. and S. Ryan, Activity Based Intelligence, Norwood, MA: Artech House, 2015, and Antony, R., Data Fusion Support to Activity-Based Intelligence, Norwood, MA: Artech House, 2015. See also the GEOINT perspective in Long, L., “Activity Based Intelligence: Understanding the Unknown,” The Intelligencer: Journal of U.S. Intelligence Studies, Vol. 20, No. 2, Fall/Winter 2013, pp. 7–15. [23] The integration of intelligence simulations and operations planning is introduced in Waltz, E., Quantitative Intelligence Analysis: Applied Analytic Models, Simulations, and Games, Rowman Littlefield, 2014. [24] In addition to AI technologies, AI-enabled commercial data sources can contribute to assessment. For example, geopolitical context may be aided by the open-source Global Database of Events, Language, and Tone (GDELT) that monitors “the world’s broadcast, print, and web news from nearly every corner of every country in over 100 languages and identifies the people, locations, organizations, themes, sources, emotions, counts, quotes, images and events.” See The GDELT Project, https://www.gdeltproject.org/. [25] Joint Chiefs of Staff, Joint Publications (JP) 2-01.3, Joint Intelligence Preparation of the Operational Environment, Joint Chiefs of Staff, Washington D.C., May 21, 2014. [26] For an explanation of the high-level process to allocate ISR assets at the platform level, see Carrillo, G. I., “Optimization Case Study: ISR Allocation in the Global Force Management Process,” Naval Postgraduate School, September 2016. [27] Ranjeev Mittu et al., “Optimization of ISR Platforms for Improved Collection in Maritime Environments,” Washington D.C.: Naval Research Laboratory, August 2009. [28] Johnson, B. W., and Green, J. M., Naval Network-Centric Sensor Resource Management, 2002, http://hdl.handle.net/10945/37809. [29] Scrofani, J. W., and Miller, D. L., “All-Domain Sensor Network Orchestration from Seabed-to-Space,” Monterey, California: Naval Postgraduate School, http://hdl.handle. net/10945/69752.

7 Sensor Distribution and Adaptation Navies have always relied on distributed communication because of the distances between vessels at sea. Line of sight (LOS) coded signaling by flag semaphores and nighttime signal lights preceded LOS radio communications between vessels and ship-to-shore communications. As ships conducted dispersed operations in the second world war, the use of the electromagnetic spectrum expanded, using VHF communications for two-way voice and coded messaging between ships and to communicate with aircraft. By the 1960s, oscillator technology enabled widespread move to longer range high frequency (HF) and ultrahigh frequencies (UHF) for fleet communications. Satellite communications and navigation systems then enhanced the ability to communicate and navigate worldwide, giving fleets significant gains in the coordination of battle groups. The adoption of narrow beam extremely high frequency (EHF) communications even allowed wideband communication from satellite to submarines with EHF antennas mounted atop periscopes. The full use of the spectrum for communication and fleet-wide access to satellite communications advanced the ability to network and coordinate distributed vessels. TDLs and submarine acoustic communications extended coordination with antiair and antisubmarine warfare platforms. The recent adoption of digital networking via TCP/IP protocols has enabled standardization for adaptive, self-synchronizing, and ad hoc networks—enabling global ashore-afloat communication of secure voice, data, and control of unmanned platforms. To distribute ISR and perform the fusion of sensors we must distinguish several key technical elements of the distribution processing as distributed sensor network (DSN):

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• Distributed detection in a DSN is the process of integrating predetection from multiple sensors to declare a detection of an object based on combined signals. This we defined as level 0 fusion in Chapter 6; in this case the sensors may be distributed and focused on a common faint source of observable energy. • Distributed tracking in a DSN consists of a set of processing nodes collecting data from sensors; when the nodes communicate, each node fuses the information received from other nodes with the local information to update its estimate on the state of the multiple target objects being tracked [1]. • Distributed tip-cueing or handoff refers to the process where one sensor in a DSN communicates a detection or track to another sensor to cause it to acquire, detect, identify, or continue (handoff ) tracking the target object. • Distributed identification is the process of obtaining sensor information from multiple sensor types (either concurrently or sequentially in time) to identify a target object by the combined signatures to discriminate the identity of a target object. In this chapter, we develop the role of digital networking to enable fully distributed operations to achieve the vision of network centric fleets with distributed ISR fusion. We illustrate with the background to the U.S. Navy development and implementation of network-centric operations. This vision was promoted in the late 1990s as a conceptual approach to moving from individual platform-centric warfighting (e.g., a vessel and its organic sensors, C2, and weapons) to a network-centric approach where platforms, sensors, a data network, weapons, and C2 are distributed to perform as a distributed system. The NCW concept was defined as: An operational concept for conducting warfare that 1) shifts the focus of control from the weapons platform to the information network that connects platforms, 2) shifts focus from independent warfighting actors to a continuously adapting ecosystem of warfighters, and 3) operates on a strategy to adapt and survive using a dynamically changing ecosystem [2].

The NCW concept, championed in the United States by Vice Admiral Arthur Cebrowski would leverage advanced information technology to improve sea power efficiency and capabilities. By 2001 the Navy developed a report, “Network Centric Operations (NCO), A Capstone Concept for Naval Operations in the Information Age” that articulated the naval benefits for flexible tactics at sea and ashore [3]. In this timeframe, Cebrowski was promoting a new



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way of war that included dispersed forces—noncontiguous operations that prefigured DMO [4]. The DoD NCW concept development period (1997–2003) included the development of a value chain to explain how networked operations contributed to combat capabilities. The value chain analysis included case studies, simulations, and real-world experiments to validate the concepts. Data standards and metrics were developed to measure the benefit of future NCW implementations [5]. The U.S. DoD promoted research in NCW through its Command and Control Research Program (CCRP) publications and conferences [6]. By 2002, the Navy established the implementation approach called FORCEnet, “the operational construct and architectural framework for Naval Warfare in the Information Age which integrates warriors, sensors, networks, command and control, platforms and weapons into a networked, distributed combat system, scalable across the spectrum of conflict from seabed to space and sea to land” [7]. FORCEnet initiated platform and equipment roadmaps that were driven by operational analyses, modeling, simulation, and war gaming, followed by naval and joint force experiments. Incremental technology improvements were made with sensors, data links, satellite sensing and communications, data fusion processing, and visualization of a common operating picture. By 2020 the Navy committed to a Naval Operational Architecture (NOA) including new networks, data standards, and formats, with battle management tools to be fielded and evaluated on a carrier strike group. NOA includes infrastructure based on cloud and edge computing, augmentation with AI capabilities, platform extension with unmanned vehicles and vessels, and full seabed-to-space interoperability. The scope and scale of an ambitious NCW implementation across a fleet the size of the U.S. Navy has moved from concept to implementation over the span of two decades and is projecting another decade to reach completion. In the next sections we extend the data fusion concepts of Chapter 6 to perform network sensing and control in support of NCW.

7.1  Sensor Networks and Grids Prior to DMO, the fleet operated primarily in clusters of vessels or battle groups with an aircraft carrier encircled by cruisers and guided missile destroyers providing air defense; destroyers with towed array sonar and submarines provided ASW defense. Sensors were local (organic sensors to the fleet), and coordination of sensors was by relatively short range, even LOS links. The introduction of DMO distributes vessels and their sensors far apart beyond line of sight (BLOS) while adding remote unmanned sensors in space, the air, and on the surface.

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The architecture of the networks (Table 7.1) that interconnect the sensors and their users refers to the topology of the links between communication nodes: • Links refer to communication channels that pass data between nodes; • Nodes refer to the communication sources or sinks of two types: • Communication endpoints nodes are sources that transmit data sensors, (human voice, digital data sources, C2 data, etc.) or sinks that receive data (processing, display, or other users of data); • Redistribution nodes provide relay of data from one link to another, to enable networking of links from sources to sinks. Networks of links for digital data enable the distribution of command, control, sensor data, Blue and Red force location data, common operating pictures, and other mission data between source platforms (e.g., seabed-to-space sensor platforms) and C2 platform to conduct operations. Table 7.1 Link and Network Topology Structures Structure Description of Use in Data Fusion Applications Point-to-point The most basic link between 2 nodes (e.g., a sensor and fusion processor, or between 2 data fusion processors). Star

Ring

Tree, hierarchy

Mesh

Peripheral sensor nodes are connected to a central fusion processing node (hub). The hub may rebroadcast data to all peripheral nodes on the network. There is no direct traffic between nodes.

Example Naval Uses Sensor node to a sensor processor.

Multiple sensors on a common platform with a local fusion processor. This is centralized data fusion. Every node has exactly two links connecting it to Sensor array with additional nodes with each node acting as a repeater limited communication to relay the data to the adjacent node. Each node operating in a line. can be a sensor node, or a sensor + fusion node. The failure of one node can impact the entire network. Individual peripheral sensor nodes transmit to and Integration of many receive from one other node only, toward a central multiple sensor node, and are not required to act as repeaters or platforms. regenerators. This can be a hierarchical connection of star subnetworks that send data to a central data fusion node. A network with at least 2 nodes with 2 or more paths A fully or nearly fully between them. Every node has a dedicated point-to- connected set of point link to every other device. sensors and data fusion processors.



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Naval communications have relied on a large variety of voice and data links to transfer data between aircraft and vessels afloat, and to ashore command and intelligence centers. This heterogeneous collection of links formed hybrid topologies that required data translation between diverse protocols to move sensor data. Navies have moved to digital networked protocols (TCP/ IP) seeking near-mesh architectures to enable a more complete exchange of sensor data (as well as common operating pictures and command data) across the diversity of links (Table 7.2). These links require assured communications in contested environments, interoperability with coalition forces, and security (confidentiality, integrity, and availability). The myriad of links from seabed to space for a deployed fleet require a significant adaptive network management capability. The organization of these many links can be identified in the following categories: • Afloat-to-ashore networks: Satellite links enable distant fleets to communicate with ashore modes (command centers, maritime intelligence centers, data storge, or other services). • Tactical wide area networks (WAN) provide surface, ship, submarine, airborne, tactical-shore, and gateway services to shore-based WANs. These nets use the higher bandwidth satellite links, VHF/HF links, and TDLs to collect sensor data to processors and users, and to distribute common operating picture and command data. • Mobile user terminal networks are narrowband networks to mobile user terminals (e.g., marines, special forces, or ground forces) from handheld to small unit terminals. • Advanced wireless networks: In the next section we introduce advanced generation wireless technologies that will enable finer-mesh networks in the future. • Undersea networks: Acoustic, fiber-optic, and short-range RF links between a sensor field involving a variety of fixed (e.g., seabed arrays) and/ or moving nodes (submarines, towed arrays, UUVs, etc.) are employed to conduct surveillance, detection, and localization of submarines or mines. These systems are often cued by an initial detection, and then multiple sensors perform a wide area search to secure multiple detections, localization, and finally classification and tracking. The deployment of a local network of sensors (e.g., sonobuoy array, or seabed barrier array) are the best approach to high probability detection-tracking after localization [9].

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Naval ISR Fusion Principles, Operations, and Technologies Table 7.2 Major Categories of Naval Optical and RF Communication Links

Communication Links VHF/HF fleet communications

Capability ·· Secure voice (analog and digital) and data modes; ·· LOS surface-to-surface and long range surface-to-air; ·· Data rates up to 1 mbps. ·· Long range extended line of sight (ELOS) capability to 4,000 km; ·· 3-kHz narrowband and 48-kHz wideband channels with 4G HF waveforms; ·· Data rates of up to 240 mbps.

Comments ·· STANAG4691 jam resistant communications; ·· LOS comm to manned, unmanned air and surface vehicles. HF long range ·· Viable alternative to SATCOM when outside the footprint of SATCOM (e.g., northern latitudes); ·· Mil-Std-188-110C/D & STANAG 4539 App H provide IP networking. TDL heterogeneous ·· NATO Link 16; ·· Time-division multiple HF/UHF networks ·· Data rates up to 2 mbps; access (TDMA) tactical data, ·· NATO Link 22; C2, mission, and weapons; ·· Data rates up to 12.7 mbps in UHF ·· Provides theater-level mode. connectivity. Ground to subsurface ·· Low bandwidth one-way instructions to ·· VLF (3–30 kHz) penetrates UHF/ELF RF data submarine to establish a different form water to a few tens of links (one-way to of two-way communication link (1 ·· Underwater links between and acoustic data mbps submarines, arrays, and links ·· Fiber optical links up to 10 km, >2 gbps; UUVs. ·· Acoustic com (ACOMM) links are not covert but can achieve ranges of 30–40 mi. TDL over P-LEO for ·· Provides beyond LOS (BLOS) ·· Satellite-based TDMA BLOS data link experimental TDL; terminal [8] with global ·· Link 16 terminals on P-LEO constellation coverage. to enable persistent overhead access. Geostationary (GEO) ·· Multiband (X, Ka) channels on geo·· GEO satellites orbit at SATCOM to very small to-surface beams; geo satellites have altitudes of 35,786 km aperture terminal multiple beams to provide shaped with an orbital period of 24 (VSAT) coverage areas; hours; round-trip latency is ·· Round-trip latency