Introduction to unmanned aircraft systems [3 ed.] 9780367366599, 0367366592


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Table of contents :
Cover
Half Title
Title Page
Copyright Page
Table of contents
Preface
Acknowledgments
About the Editors
Contributors
1 History
1.1 The Beginning
1.2 The Need for Effective Control
1.3 The Radio and the Autopilot
1.4 The Aerial Torpedo: The First Modern Unmanned Aircraft (March 6, 1918)
1.5 The Target Drone
1.6 WWII U.S. Navy Assault Drone
1.7 WWII German V-1 Buzz Bomb
1.8 WWII German Mistletoe
1.9 Early Unmanned Reconnaissance Aircraft
1.10 Radar Decoys: 1950s–1970s
1.11 Long-Range Reconnaissance Unmanned Aircraft Systems: 1960s–1970s
1.12 First Helicopter Unmanned Aircraft Systems: 1960s–1970s
1.13 The Hunt for Autonomous Operation
1.14 The Birth of the Twin Boom Pushers
1.15 Desert Storm: 1991
1.16 Overcoming the Manned Pilot Bias
1.17 Amateur-Built Unmanned Aircraft
1.18 Will Unmanned Aircraft Systems Replace Manned Aircraft?
Discussion Questions
Notes
2 UAS Applications
2.1 Introduction
2.2 Basic Technology
2.2.1 Control Methods
2.2.1.1 Manual Control
2.2.1.2 Stabilized Control
2.2.1.3 Automated Control
2.3 Payloads
2.3.1 Remote Sensing
2.3.2 Passive Electro-Optical Sensors
2.3.2.1 Electro-Optical Imaging System
2.3.2.2 Visible RGB Sensors
2.3.2.3 Full-Motion Video Sensors
2.3.2.4 IR/NIR/SWIR Sensors
2.3.2.5 MWIR/LWIR Sensors
2.3.3 Active Sensors
2.3.3.1 LiDAR
2.3.3.2 Radar and Synthetic Aperture Radar
2.4.1 UAS Fleet Management Software
2.4 UAS Software for Commercial Applications
2.4.2 Autopilot Software
2.4.3 Sensor Data Asset Management
2.4.4 Analytical Photogrammetry Software
2.4.5 Change Detection and Machine Learning
2.4.6 Computer Vision
2.4.6.1 Autonomous Flight Path Algorithms
2.5 Commercial Applications
2.5.1 Building and Roof Inspections
2.5.2 Aircraft Inspections
2.5.3 Oil, Gas, Power Lines, and Nuclear Power Plants
2.5.4 Industrial Inspection
2.5.5 Civil Infrastructure
2.5.6 Electric Power Industry
2.5.7 Wind Turbine Inspection
2.5.8 Tower/Antenna Inspection
2.5.9 Oil and Gas Inspection
2.5.10 Photogrammetric Applications
2.5.11 Aerial Mapping
2.5.12 Aerial Surveying
2.5.13 Volumetrics
2.5.14 Precision Agriculture
2.5.15 Natural Resource Management
2.5.16 Aerial Filming and Photography
2.5.17 Filmmaking
2.5.18 Real Estate
2.5.19 Marketing
2.5.20 News Reporting
2.5.21 Intelligence, Surveillance, Reconnaissance, and Emergency Response
2.5.22 Law Enforcement
2.5.23 Search and Rescue
2.5.24 Signals Intelligence
2.5.25 Communications Relay
2.5.26 Atmospheric Information Collection
2.5.27 Meteorology
2.5.28 Hazardous Material Detection
2.5.29 Radioactive Material Detection
2.5.30 Applications Requiring Physical Interaction with Substances, Materials, or Objects
2.5.31 Aerial Chemical Application
2.5.32 Water Sampling
2.5.33 Small Unmanned Cargo Aircraft Delivery
2.5.34 Large Unmanned Cargo Delivery
2.6 Additional Considerations
2.6.1 Mission Planning
2.6.2 Data Processing and Analysis
2.7 Conclusion
Discussion Questions
Note
References
3 The “System” in UAS
3.1 Introduction
3.1.1 What Makes Up an Unmanned Aircraft System
3.2 UAS/RPA
3.2.1 Fixed Wing
3.2.2 Vertical Takeoff and Landing
3.2.3 Hybrid Platforms
3.3 Command and Control Element
3.3.1 Autopilot
3.3.2 Ground Control Station
3.4 Communication Data Link
3.4.1 Radio Line-of-Sight
3.4.2 Beyond Radio Line-of-Sight
3.5 Payload
3.5.1 Electro-Optical
3.5.2 Thermal Infrared
3.5.3 Spectral
3.5.4 Laser
3.6 Launch and Recovery
3.7 Human Element
Discussion Questions
4 UAS Sensing – Theory and Practice
4.1 Why We Fly
4.2 Introduction to Sensing
4.2.1 In Situ Sensing
4.2.2 Remote Sensing
4.2.3 Platform Considerations
4.3 Remote Sensing
4.3.1 Overview
4.3.2 Sensor Types
4.3.2.1 Spot Sensors
4.3.2.2 Imaging Sensors
4.3.3 Common Sensors
4.3.3.1 Visible Spectrum Cameras and Near-Infrared Cameras
4.3.3.2 Long-Wave Infrared Cameras
4.3.3.3 Hyperspectral Imagers
4.3.3.4 LiDAR
4.3.3.5 Synthetic Aperture Radar
4.4 Geospatial Data Types
4.4.1 Raster Data
4.4.2 Vector Data
4.5 Image Processing Concepts
4.5.1 Structure from Motion
4.5.1.1 Point Clouds
4.6 Data Management
4.6.1 Data Security (Cloud Security)
4.6.2 Long-Term Data Storage
4.7 Applications
4.7.1 Motion Imagery
4.7.2 Emergency Response
4.7.3 Map (Background) Imagery
4.7.4 Infrastructure Inspection
4.7.5 Vegetation Health Measurements
4.7.5.1 Vegetation Index: An Overview
4.7.5.2 UAS in Agriculture-Vegetation Indices
4.7.5.3 Thermal Mapping
4.7.5.4 Broader Vegetation Management
4.7.5.5 Airframes for Vegetation Applications
4.8 Conclusions
Discussion Questions
Bibliography
5 UAS Regulations, Standards, and Guidance
5.1 Introduction
5.2 U.S. Aviation Regulatory System
5.2.1 History of U.S. Aviation Regulations
5.2.2 Federal Aviation Administration
5.2.3 Enforcement and Sanctions
5.3 Current U.S. Regulation of Unmanned Aircraft
5.4 How the Process Works
5.5 Standards and Guidance versus Regulations
5.6 International Aviation Regulations
5.7 Other Nations’ Domestic Regulatory Efforts
5.8 The Way Forward: The Future of Unmanned Aircraft Systems Regulations
5.9 Conclusion
Discussion Questions
Notes
6 Human Factors in Unmanned Aerial Systems
6.1 Introduction
6.2 The Enormity of the Scope
6.3 A Caution Regarding Hindsight Bias
6.4 Human Perception and RPA Operations
6.5 Attention
6.6 Selective Attention
6.7 Focused Attention
6.8 Divided Attention
6.9 Sustained Attention
6.10 Human Error
6.11 Threat and Error Management
6.12 Crew Resource Management
6.13 Situation Awareness
6.13.1 Vigilance
6.13.2 Diagnosis
6.13.3 Risk Analysis
6.13.4 Action
6.14 Human–Machine Interfacing
6.15 Compatibility
6.16 Compatibility Types
Recommended Readings
Discussion Questions
References
7 Safety Assessments
7.1 Introduction
7.2 Hazard Analysis
7.2.1 Purpose
7.2.2 Preliminary Hazard List
7.2.3 Preliminary Hazard Analysis
7.2.4 Operational Hazard Review and Analysis
7.2.5 Change Analysis
7.3 Risk Assessment
7.3.1 Purpose
7.3.2 Development
7.3.3 Use
7.4 Safety Evaluation
7.4.1 Risk Assessment
7.4.2 Flight Test Cards
7.4.3 Airworthiness Certification
7.5 Accident Investigation Considerations
7.5.1 Software and Hardware
7.5.2 Human Factors
7.5.3 Suggestions
7.6 Conclusion and Recommendations
Discussion Questions
References
8 Export Control and ITAR
8.1 Introduction
8.2 Glossary of Terms for Export Control Understanding
8.3 The Sources of Export Controls
8.4 What Is Export Control?
8.5 Where Do Export Controls Come From?
8.5.1 Export Control Reform Act and UAS
8.6 Export Administration Regulations
8.6.1 Commerce Control List (CCL)
8.6.2 Missile Technology Control Regime Annex
8.7 International Traffic in Arms Regulation (ITAR)
Category VIII – Aircraft, Space, and Associated Equipment
Category XI – Military and Space Electronics
Category XV – Spacecraft Systems and Associated Equipment Aircraft
Other USML Categories Also Have the Potential to Include Items Relevant to USML Controls
8.8 How Do Export Control Issues Come Up in Real Life?
8.9 How to Protect Export-Controlled Products and Information (“Know How”)?
8.10 What Are Export Control Violations?
8.11 How Do We Perform Work Outside of the United States?
Discussion Questions
Notes
9 Unmanned Aircraft System Design
9.1 Introduction: Mission Capability-Derived Design
9.2 The UAS Design Process
9.2.1 Design Tools
9.2.2 Design Automation and Optimization
9.3 Unmanned Aircraft Subsystems
9.3.1 Airframe
9.3.2 Propulsion System
9.3.3 Flight Control System
9.3.4 Control Station
9.3.5 Payloads
9.3.6 Communications, Command, and Control (C3)
9.4 Standards for UAS Design, Construction, and Operations
9.5 UAS Design Verification and Mission Validation
9.6 Design Characteristics for UAS
Discussion Questions
References
10 UAS Airframe Design
10.1 Introduction
10.2 A Few Observations Regarding UAS Design
10.2.1 Form Follows Function: The Best Place to Begin the Design Process
10.2.2 Economic Influences on the Design Process
10.2.3 Exogenous Factors Affecting the Design of UASs
10.2.4 Selected Preliminary Comments Relevant to UAS Flight Dynamics and Physics
10.3 Airframe Designs
10.3.1 Fixed-Wing Designs
10.3.1.1 Factors in UAS Tail Designs
10.3.1.2 Conventional Wing, Inverted-T-Tail Aircraft
10.3.1.3 Conventional Fuselage, Aft Engine Designs
10.3.1.4 Twin-Boom, Pusher-Propeller Designs
10.3.1.5 Flying Wings
10.3.1.6 Canard UASs
10.3.2 Rotating-Wing or Rotary-Wing Designs
10.3.2.1 Helicopter UAS
10.3.2.2 Multirotors
10.3.2.3 Other Rotating-Wing UASs
10.4 Launch and Recovery Systems
10.5 Conclusion
Discussion Questions
References
11 UAS Propulsion System Design
11.1 Introduction
11.2 Engine Design
11.2.1 Reciprocating Engines
11.2.1.1 Four-Cycle Engines
11.2.1.2 Two-Cycle Engines
11.2.1.3 Diesel Engines
11.2.2 Wankel or Rotary Powerplants
11.2.3 Gas Turbine Engines
11.2.3.1 Turboprop and Turboshaft Engines
11.2.3.2 Turbofan Engines
11.2.3.3 Turbojets
11.2.4 Electric Motors
11.3 Propellers and Rotors on UASs
11.4 Propulsion System Design
11.4.1 Engine Subsystems
11.4.2 Propulsion System Installation
11.4.3 Hybrid Electric Systems
11.5 Safety Evaluation
11.5.1 Reliability and Risk Assessment
11.5.2 Certification
11.6 Maintainability
11.7 Conclusion
Discussion Questions
References
12 UAS Subsystem Nexus: : The Electrical System
12.1 Introduction
12.2 UAS Electrical Systems: General Characteristics
12.3 sUAS Electrical Systems
12.3.1 All-Electric sUAS
12.3.1.1 Power Sources for All-Electric sUAS
12.3.1.2 Electric sUAS Propulsion
12.3.2 Nonelectrically Powered sUAS
12.4 Electrical Systems for Large UASs
12.5 Conclusion
Discussion Questions
References
13 Unmanned Aircraft Systems (UAS) Communications
13.1 Introduction
13.2 Electromagnetic Wave (EM) Propagation
13.2.1 The Electromagnetic Spectrum
13.2.2 Electromagnetic Wave Propagation in Free Space
13.3 Basic Communication System and Its Elements
13.3.1 Modulation
13.3.2 Transmitter
13.3.2.1 Frequency Hopping Technique for Transmission
13.3.3 Channel
13.3.3.1 Antenna Directivity
13.3.3.2 Antenna Gain
13.3.3.3 Antenna Polarization
13.3.4 Receiver
13.3.4.1 Signal to Noise Ratio
13.3.4.2 Receiver Sensitivity
13.3.4.3 Despreading the Signal
13.3.5 Demodulation
13.4 System Design
13.4.1 Establishing Bandwidth Requirements
13.4.2 Link Design
13.4.2.1 Reflection at Antenna–Cable Junction
13.4.2.2 Losses at the Transmitting Antenna
13.4.2.3 Losses due to Free Space Propagation
13.4.2.4 Power Received at the Receiving Antenna
13.4.2.5 Power in Decibel Milliwatt
13.4.2.6 Signal-to-Noise Ratio at the Receiver
13.4.2.7 Calculation of Signal-to-Noise Margin from Receiver Sensitivity
13.5 Summary of Design Principles
13.6 Associated Problems from EMI Interference, Jamming, and Multipath
13.6.1 EMI Interference
13.6.2 Jamming
13.6.3 Multipath
13.7 Review Questions
Discussion Questions
References
14 Command and Control
14.1 Introduction
14.2 Human Element
14.3 Datalinks
14.3.1 RF Spectrum and FCC
14.3.2 Line-of-Sight Communication
14.3.3 Beyond Line-of-Sight Communication
14.3.4 Communication Protocols
14.3.4.1 MAVLink Protocol
14.3.4.2 MAVLink Header Structure
14.3.4.3 MAVLink Message (Payload) Structure
14.3.5 Error Detection/Correction
14.3.6 Encryption
14.4 UAS Flight Control
14.4.1 Autopilot Systems
14.4.2 Sensors and Components
14.4.3 Tuning
14.5 Large UAS
14.5.1 IMU/INS Stabilization Systems
14.5.2 Additional Navigation Options
14.5.3 Launch and Recovery
14.6 Open Source
14.7 Conclusion
Discussion Questions
References
15 Unmanned Aircraft Subsystem Integration
15.1 The Design Process
15.2 Mission Statement and Objectives
15.3 Concept Development and Trade Studies
15.4 Preliminary Design Review
15.5 Critical Design Review
15.6 Fabrication
15.7 System Testing
15.8 Flight Testing
15.9 Concluding Remarks
Discussion Questions
References
16 Detect and Avoid
The MITRE Corporation
16.1 Introduction
16.1.1 UAS as a Transformational Technology
16.1.2 Standards as a Driver for UAS Integration
16.2 Regulatory Basis
16.3 Functions of DAA System
16.3.1 Remain Well Clear
16.3.2 Collision Avoidance
16.3.3 Detect and Avoid: Subfunctions
16.4 Process and Functions of a DAA System
16.4.1 “Observe” Tasks
16.4.1.1 Detect Target
16.4.1.2 Track Target
16.4.1.3 Combine Target Tracks
16.4.2 “Orient” Tasks
16.4.2.1 Identify Object
16.4.2.2 Evaluate Threat
16.4.2.3 Prioritize Threat
16.4.3 “Decide” Tasks
16.4.3.1 Declare/Alert
16.4.3.2 Determine Maneuver
16.4.4 “Act” Tasks
16.4.4.1 Command Maneuver
16.4.4.2 Execute Maneuver
16.4.4.3 Return to Course
16.5 The Role of the Pilot
16.5.1 Pilot in-the-Loop
16.5.2 Pilot on-the-Loop
16.5.3 Pilot Off-the-loop
16.6 The Role of Air Traffic Control
16.7 DAA System Components
16.7.1 Surveillance
16.7.2 Avoidance Algorithms
16.7.3 Displays
16.8 Detect and Avoid in the Terminal Area
16.9 Conclusion
Acknowledgments
Discussion Questions
Note
References
17 UAS in Public Safety
17.1 UAS in Public Safety: Introduction
17.2 UAS in Public Safety: Laws and Regulations
17.3 UAS in Public Safety: Policy
17.4 UAS in Public Safety: Enabling Technology
17.5 UAS in Public Safety: Training the Operator
17.6 Conclusion
Discussion Questions
18 Cybersecurity Counter Unmanned Aircraft Systems (C-UAS) and Artificial Intelligence (AI)
18.0 Problem – The Risk of Terrorist Attack vs. U.S. Air Defense System
18.0.1 Contributing Technologies
18.0.2 Attack/Defense Scenarios
18.0.3 Chapter 18 Plan
18.1 Description of the sUAS/UAS Landscape
18.1.1 Autonomy vs. Automation Levels
18.1.2 UAS Collaboration
18.2 Establishment of a Risk Metric and Attack/Defense Scenarios
18.2.1 Risk
18.2.2 Attack/Defense (A/D) Scenario Analysis
18.3 Discussion of Conventional Vulnerabilities of Air Defense Systems (ADS), Attacks by sUASs, and Countermeasures
18.3.1 What Is the Counter-UAS Problem?
18.3.2 Operational Protection from Hostile UAS Attacks – A Helicopter View
18.3.3 Countering UAS Air Threats
18.3.4 Vulnerabilities Perspective
18.3.5 Conventional Vulnerabilities of Air Defense Systems (ADS), Attacks by sUAS, and Countermeasures
18.3.6 Conventional Countermeasures against sUAS / UAS
18.3.6.1 Active Measures
18.3.6.2 Passive Measures
18.3.7 Aggressor Counter-Countermeasures Specific to UAS Deployment – SWARM
18.4 UAS Sense and Avoid Systems (SAA)
18.4.1 Airborne Sensing Systems (AS)
18.4.2 Sensor Parameters
18.4.3 Autopilot
18.4.4 SAA Services and Subfunctions
18.5 SCADA
18.5.1 “UAS Are Just Flying SCADA Machines!” (Nichols R.-0., 2016)
18.5.2 SCADA Cyber Vulnerabilities
18.5.3 SCADA Cyberattack Vectors
18.5.4 Cyberattack Taxonomy
18.5.4.1 Espionage
18.5.4.2 Software-Based Vulnerabilities
18.5.4.3 Insider Threat Vulnerabilities
18.5.4.4 Hardware-Based Vulnerabilities
18.5.4.5 Wireless Attacks
18.5.4.6 General Attack Possibilities
18.6 Counter Unmanned Aircraft Systems (C-UAS)
18.6.1 Active sUAS/UAS Countermeasures
18.6.2 Passive sUAS/UAS Countermeasures
18.6.3 Aggressor Counter-Countermeasures Specific to UAS Deployment
18.6.4 Designing for Stealth
18.6.5 Design to Acceptable Risk Level
18.6.6 Detection Signatures
18.6.7 Acoustical Signatures
18.6.8 Acoustic Signature Reductions
18.6.9 Acoustical Detection Issues
18.6.10 MEMS Gyroscope
18.6.11 Resonance Effects on MEMS
18.6.12 Countermeasures to Acoustic Attack – Gyroscopes
18.6.13 Resonance Tuning
18.6.14 SWARM C-UAS Functionality and Threats
Destructive countermeasures include
Nondestructive Countermeasures include
18.6.15 SWARM C-UAS Functionality Challenges
18.6.16 Counter-UAS as Disruptive Technology
18.6.17 Joint Forces C-UAS Challenges
18.7 Conclusions
18.8 Discussion Topics
Notes
Bibliography
19 Unmanned Traffic Management (“UTM”)
Note
20 The Future of Unmanned Aircraft Systems
20.1 Introduction
20.2 Anticipated Market Growth
20.3 The Future of UAS Market Segments
20.3.1 Private/Commercial UAS Market Segment
20.3.2 Public UAS Market Segment
20.3.3 Predicates to Future Market Access
20.3.3.1 Routine Airspace Access
20.3.3.2 Training and Certification
20.4 The Potential for Career Opportunities
20.5 Emerging Trends in Technology
20.5.1 Miniaturization
20.5.2 Power Solutions
20.5.2.1 Alternative Energy
20.5.2.2 Electric Options
20.5.3 Materials Improvements
20.5.4 Revolutionary Manufacturing
20.5.5 Computing and Artificial Intelligence
20.6 Future Applications
20.6.1 Atmospheric Satellites
20.6.2 Air Transportation
20.6.3 Unmanned Combat Air Vehicle
20.6.4 Commonality/Scalability
20.6.5 Swarming UAS
20.7 Five Years and Beyond
Discussion Questions
References
Epilogue
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18
Chapter 19
Chapter 20
Index
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i

Introduction to Unmanned Aircraft Systems

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Introduction to Unmanned Aircraft Systems Third Edition

Edited by R. Kurt Barnhart, Douglas M. Marshall, and Eric J. Shappee

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Third edition published 2021 by CRC Press 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-​2742 © 2021 selection and editorial matter, Douglas M. Marshall, Richard K. Barnhart, and Eric J. Shappee; individual chapters, the contributors First edition published by CRC Press 2011 Second edition published by CRC Press 2016 CRC Press is an imprint of Informa UK Limited The right of Douglas M. Marshall, Richard K. Barnhart, and Eric J. Shappee to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-​750-​8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. British Library Cataloguing-​in-​Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-​in-​Publication Data Names: Marshall, Douglas M., 1947– editor, author. | Barnhart, Richard K., editor, author. | Shappee, Eric, editor, author. Title: Introduction to unmanned aircraft systems/edited by Douglas M. Marshall, Richard K. Barnhart and Eric Shappee. Description: Third editon. | Boca Raton: CRC Press, 2021. | Includes bibliographical references and index. Identifiers: LCCN 2020035929 (print) | LCCN 2020035930 (ebook) | ISBN 9780367366599 (hardback) | ISBN 9780429347498 (ebook) Subjects: LCSH: Drone aircraft. | Drone aircraft–Automatic control. Classification: LCC TL589.4 .I68 2021 (print) | LCC TL589.4 (ebook) | DDC 629.133/3–dc23 LC record available at https://lccn.loc.gov/2020035929 LC ebook record available at https://lccn.loc.gov/2020035930 ISBN: 978-​0-​367-​36659-​9  (hbk) ISBN: 978-0-367-68608-6 (pbk) ISBN: 978-​0-​429-​34749-​8  (ebk) Typeset in Palatino by Newgen Publishing UK

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Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix About the Editors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi List of Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 1 History. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Douglas M. Marshall and Benjamin Trapnell 2 UAS Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Mark Patrick Collins 3 The “System” in UAS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Joshua Brungardt and Kurt Carraway 4 UAS Sensing – Theory and Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Gabriel B. Ladd 5 UAS Regulations, Standards, and Guidance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Douglas M. Marshall 6 Human Factors in Unmanned Aerial Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 Warren Jensen 7 Safety Assessments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Eric J. Shappee and Graham Feasey 8 Export Control and ITAR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Eric McClafferty and Rose Mooney 9 Unmanned Aircraft System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Robert D. Love and Brian Argrow 10 UAS Airframe Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Michael T. Most and Michael Stroup 11 UAS Propulsion System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Michael T. Most and Graham Feasey 12 UAS Subsystem Nexus: The Electrical System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 Michael T. Most and Samuel Stewart

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Contents

13 Unmanned Aircraft Systems (UAS) Communications . . . . . . . . . . . . . . . . . . . . . . . . . 291 Saeed M. Khan 14 Command and Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321 Nathan Maresch 15 Unmanned Aircraft Subsystem Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 William H. Semke 16 Detect and Avoid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Dallas Brooks, Stephen P. Cook, and Brandon Suarez 17 UAS in Public Safety. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 Benjamin Miller and Adam Trojanowski 18 Cybersecurity Counter Unmanned Aircraft Systems (C-​UAS) and Artificial Intelligence (AI). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 Randall K. Nichols 19 Unmanned Traffic Management (“UTM”). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 Michael S. Baum 20 The Future of Unmanned Aircraft Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 Tom Haritos and R. Kurt Barnhart Epilogue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459 Douglas M. Marshall Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477

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Preface It is with great pleasure that we present this greatly expanded third edition of Introduction to Unmanned Aircraft Systems. The unmanned aircraft systems (UAS) industry continues to be a highly dynamic and constantly evolving industry with the advancement of science and technological enablement. With that in mind, the aim of this expanded edition is to identify and survey the fundamentals of UAS operations, which will serve both as a basic orientation to UAS and as a foundation for further study. Content herein is suitable for both introductory and advanced systems studies. This work was originally birthed out of an unsuccessful search for suitable texts for such a course. As in the first two editions, the chapters have been individually contributed by some of the nation’s foremost experts in UAS operations from all corners of both higher education and industry; therefore, the reader may note some variation in writing style. It was decided to leave the contributions in this form in the interest of preserving the author’s intent, thereby improving the quality of information contained herein. It is the editors’ suggestion that introductory students focus on Chapters 1–​7, 9, 15, 17, 19, and 20 for a total of 12 chapters. The remainder can be used at the instructor’s discretion and for advanced technical study. This publication would not have been possible without the close cooperation of all the editors and contributors; a heartfelt thank you to all who gave of your time to make this possible. Your feedback is welcomed as a basis for future editions of this book as the industry continues to advance.

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Acknowledgments As editors we would first like to thank our contributing authors for their valuable time and expertise. Without you, this book would simply not have nearly the breadth and depth that it does with your input. We also would like to thank Beth Drescher, who spent many hours on first read-​throughs and working on format consistency. Beth, your expertise made the burden of such a large text manageable. We would also like to send out a note of thanks to our spouses, Anita, Sandi, and Lisa, who let us give up hours of our time and in some cases helped us with syntax and flow. We thank you!

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About the Editors Dr. R. Kurt Barnhart is Professor and currently the Associate Dean of Research at Kansas State University Salina, in addition to serving as the executive director of the Applied Aviation Research Center, which established and now oversees the Unmanned Aerial Systems program office. Most recently, Dr. Barnhart was the Head of the Aviation Department at Kansas State University. He is a member of the graduate faculty at K-​State and holds a commercial pilot certificate with instrument, multiengine, seaplane, and glider ratings. He also is a certified flight instructor with instrument and multiengine ratings and also holds an airframe and powerplant certificate with inspection authorization. Dr. Barnhart holds an AS in Aviation Maintenance Technology from Vincennes University, a BS  in aviation administration from Purdue University, an MBAA from Embry-​Riddle Aeronautical University, and a PhD  in educational administration from Indiana State University. Dr. Barnhart’s research agenda is focused in aviation psychology and human factors as well as the integration of Unmanned Aircraft Systems into the National Airspace System. His industry experience includes work as an R&D inspector with Rolls Royce Engine Company, where he worked on the RQ-​4 Unmanned Reconnaissance Aircraft development program, as well as serving as an aircraft systems instructor for American Trans-​ Air airlines. Formerly, Dr.  Barnhart was an Associate Professor and Acting Department Chair of the Aerospace Technology at Indiana State University, where he was responsible for teaching flight and upper division administrative classes. Courses taught include Aviation Risk Analysis, Citation II Ground School, King Air 200 Flight, Air Navigation, Air Transportation, Instrument Ground School, and many others. Douglas M.  Marshall, J.D., is the founder and owner of TrueNorth Consulting LLC, a UAS support and service organization founded in 2007. He enjoyed an appointment as an Adjunct Professor of Law, DePaul University College of Law, where he developed and delivered the first drone law course in an American law school. Previously, he was a division manager, UAS Regulations & Standards Development at the Physical Science Laboratory, New Mexico State University, and professor of aviation at the University of North Dakota. He has been engaged full time on UAS-​related activities for over 15 years, is the co-​editor of two books related to aviation, and is the author of numerous published articles on aviation law, regulations, and remotely piloted aircraft. He has served on RTCA SC-​203, ASTM F-​38, and SAE G-​10 Committees; the AUVSI Advocacy Committee; the Arctic Monitoring and Assessment Program UAS Expert Group; the Small UAS Aviation Rulemaking Committee; and the Part 91 Working Group supporting a second UAS Aviation Rulemaking Committee. He chaired the ASTM F38.02.01 Task Group on Standards for Operations Over People; was a member of the Steering Committee, Civil Applications of Unmanned Aerial Systems Conference, Boulder, Colorado, and several other committees dedicated to the development of UAS; and has delivered presentations on international aviation regulations and airspace issues at symposia and conferences around the world. Eric J. Shappee is a Professor of Aviation at Kansas State University Polytechnic. He has served as the Director of Flight Operations, Acting Department Head, and Professional Pilot Program Lead for Aviation. He is a member of the Aviation Accreditation Board International (AABI) and has served on the Board of Trustees for the University Aviation Association (UAA). Eric teaches numerous aviation courses, which include: Introduction xi

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to Aviation, Commercial Pilot Ground School, Aviation Safety, and Aviation Accident Investigation. He holds a commercial pilot certificate with instrument, multiengine, and glider ratings. He is also a certified flight instructor with gold seal and a Master CFI. Eric holds two associate degrees from Antelope Valley College (Lancaster, California): a bachelor degree in aeronautical science and a master degree in aeronautical science and safety from Embry-​Riddle Aeronautical University. His main area of focus is in aviation safety. He has developed several risk assessment tools for K-​State and other aviation organizations. Further, he is a member of the International Society of Air Safety Investigators and has attended the NTSB Academy. He has been active in the field of aviation since 1986, and teaching since 1995. Eric has given over 3000 hours of dual given. During his career in aviation, Professor Shappee has also spent time working with unmanned aerial systems.

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Contributors Brian Argrow is a professor of aerospace engineering sciences and founding director (emeritus) of the Research and Engineering Center for Unmanned Vehicles (RECUV) at the University of Colorado Boulder. Professor Argrow is an author or coauthor of more than 100 journals and conference proceedings, and he received numerous teaching and education awards. He is chair-​emeritus of the AIAA Unmanned Systems Program Committee. He received the Air Force Exemplary Civilian Service Award for service on the Air Force Scientific Advisory Board (2003, 2005–​2009) where he coauthored two reports on UAS and served on the NASA Advisory Council’s UAS Subcommittee from 2011 to 2013. In 2014, he cofounded and now codirects the Unmanned Aircraft System and Severe Storms Research Group (USSRG). Professor Argrow currently serves on the ASTM F38 Subcommittee for Small VTOL UAS Operations over People. Michael S. Baum, JD, MBA, ATP, is an aviation consultant, and founder and Permanent Editorial Board member of the Aviators Code Initiative (ACI). He contributes actively in various fora developing manned and unmanned aviation standards and safety best practices. He served as VP and executive committee member at VeriSign (VRSN); IT security law and policy consultant, Independent Monitoring, LLC; Network Analyst, BBN Technologies (now Raytheon). Prior thereto he practiced law in Boston. He was Founder and Chair, Electronic Commerce Division and Information Security Committee, Section of Science and Technology Law, American Bar Association (ABA); Observer, United Nations Commission on International Trade Law (UNCITRAL); Chairman, International Chamber of Commerce (ICC Paris) ETERMS Working Party; and Vice Chairman, ICC Electronic Commerce Commission; Board Member: ANSI ASC X12; The Jurimetrics Journal (ABA/​ASU); The PKI Forum; the Charles Babbage Foundation; and member of various IT enterprise advisory boards. Publications include:  co-​author, UAS Pilots Code (ACI); Aviators Model Code of Conduct (series, ACI); Federal Certification Authority Liability and Policy (NIST/​MITRE); co-​author (with Ford), Secure Electronic Commerce (1st & 2nd Eds., Prentice-​ Hall); co-​author, Digital Signature Guidelines (ABA); co-​author (with Perritt) Electronic Contracting, Publishing and EDI Law (Wiley); and contributor, EDI and the Law (Blenheim). Awards include: ABA Committee on Cyberspace Law’s Excellence Award; National Notary Association’s Achievement Award; Journal of EDI, EDI Pioneer, and LightHawk’s President’s Award. Baum is a graduate of Western New England University School of Law and is a member of the Massachusetts Bar. He received his MBA from the Wharton School at the University of Pennsylvania. He is an FAA-certificated Airline Transport Pilot in single engine aircraft, and holds Private Pilot certification with airplane single engine sea, multiengine land, rotorcraft-helicopter, and glider ratings. He is also an Advanced Ground Instructor. Dallas Brooks is the director of UAS Research and Development for New Mexico State University’s Physical Science Laboratory (PSL). He is responsible for NMSU’s broad spectrum of government and commercial UAS research, development, test, and engineering (RDT&E) programs. A  recognized national leader in unmanned systems integration, he engages and coordinates with national and international regulatory, support, and administrative agencies to help ensure that the tremendous capabilities of unmanned systems are realized. Brooks’ aviation and technical experience spans over xiii

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30 years, primarily in service to America’s armed forces at home and overseas. He serves on multiple national-​level boards, committees, and steering groups. He is the executive vice chairman and the strategic planning committee chairman of the National Board of Directors for the Association of Unmanned Vehicle Systems International (AUVSI), the world’s largest nonprofit organization devoted exclusively to advancing the unmanned systems and robotics community. He cochairs the federal multiagency UAS Sense and Avoid Science and Research Panel, supporting the FAA, the DoD, NASA, and DHS to identify and solve key sense-​and-​avoid challenges. He is also a member of the FAA’s UAS Aviation Rulemaking Committee and serves on the Executive Council of the FAA’s UAS Center of Excellence. Joshua Brungardt is the executive vice president for PARADIGMisr in Bend, Oregon. He is also a courtesy faculty member of Oregon State University’s College of Forestry. He has served as the Unmanned Aircraft System (UAS) director for Kansas State University helping to grow one of the largest and top renowned UAS Academic programs in the United States. Brungardt served on the Kansas Governor’s Aviation Advisory Council from 2010 to 2012 and is currently a member of the board of directors for the Oregon Aviation Industries Board. In 2010, Brungardt attended senior officer training on the Predator UAS at Creech AFB with the 11th Reconnaissance SQ. In his positions, and as a consultant to numerous UAS companies, Brungardt has written over 80 approved UAS Certificates of Authorization from the FAA. Brungardt has also been chief pilot for High Performance Aircraft Training, EFIS Training, and Lancair Aircraft. He holds ATP & CFII ratings with over 4000 hours, as well as having raced at the Reno National Air Races. In addition to completing over 75 first flights on experimental aircraft, he has served as an instructor and test pilot to the U.S. Air Force. In 2006, Brungardt founded the pilot training company EFIS Training, which focused on pilots transitioning to glass cockpits. Brungardt received a bachelor’s degree in airway science and an associate’s degree in professional piloting from Kansas State University in 2002. In 2013, Brungardt was awarded the Alumni Fellow of the Year Award from KSU’s College of Technology and Aviation. Kurt J. Carraway, a retired Colonel with 25 years of service in the United States Air Force, is K-​State Polytechnic’s Unmanned Aircraft Systems (UAS) Research Executive Director. In this capacity, he provides strategic leadership in advancing Kansas State University’s UAS program goals. He directs the execution of research activities involving UAS through the Applied Aviation Research Center (AARC). Carraway also directs flight operations development and maturation of the UAS training program through direct supervision of the flight operations staff. He manages highly skilled UAS professionals who perform hundreds of UAS flights per year in civil airspace. He sets policies and procedures for unmanned flight operations. He serves as Principal Investigator (PI) on UAS activities through the AARC and is the University PI representative to ASSURE, the FAA’s UAS Center of Excellence. In that role, Carraway also serves as the UAS training focal point lead. He is a professor, an instructor, and a mentor to students. Before arriving at K-​ State Polytechnic, Carraway was stationed at Camp Smith in Oahu, Hawaii, where he served first as Joint Operations Director and then Division Chief of Current Operations, both for the U.S. Pacific Command. Carraway worked with the Global Hawk UAS, as an evaluator and instructor pilot, and later became commander of the Global Hawk squadron. Carraway established standard operating procedures and composed technical manuals for the military’s use of the Global Hawk. A  native of St. Louis, Missouri, Carraway received a Bachelor of Science in Mechanical Engineering at Missouri University of Science and Technology in Rolla, prior to entering the Air Force. During his service, Carraway

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also completed a Master of Science in Systems Engineering at the Air Force Institute of Technology on the Wright-​Patterson Air Force Base in Dayton, Ohio, and a Master of Arts in Management from Webster University in St. Louis, Missouri. He is married to Pamela (nee) Savage and has two daughters, ages 19 and 15. Mark Patrick Collins is an Associate Professor and Director of Unmanned Systems (UMS) at Indiana State University (ISU). His responsibilities include instructing UMS courses to undergraduate students, developing new courses, advising students, developing grant opportunities, researching ways unmanned cargo aircraft (UCA) can operate in the national airspace, and working with the community. He is a Doctoral Student at Unmanned Vehicle University in Unmanned Systems Engineering, has a MS  in Aviation & Transportation from Lewis University, a BS  in Industrial Technology from Southern Illinois University (SIU), and A.S. in Aviation Maintenance from SIU. His FAA certifications include remote drone pilot, private pilot, airframe, and powerplant certificates. It is his long-​term goal to start the first large unmanned cargo airline (LUCA) in the National Airspace System (NAS). He has received international recognition from the Platform of Unmanned Cargo Aircraft for his research in LUCA in the NAS. He is a business owner and entrepreneur. He owns a sign manufacturing business in Elkader Iowa called Signs N Frames. He and his wife also manage a property management business across three states and two countries. Before starting these businesses, he worked at Atlas Air Cargo Airline from 1999 to 2003 as an aircraft heavy check site representative and as a purchasing manager. His responsibilities at Atlas Air included purchasing labor and fuel for 50+Boeing 747 aircraft from places such as Singapore, Netherlands, Hong Kong, Germany, and mainland China. Before Atlas Air, he worked at AAR Allen Aircraft in Wood Dale, Illinois as Site Representative from 1995 to 1999 and was in the Army Illinois National Guard from 1990 to 1996. Besides his career goals, Professor Collins is happily married, is a 4th Degree Knight of Columbus member at his church, is working on his instrument rating, and plans on purchasing a Lancair IVP airplane someday. Stephen P.  Cook is a principal safety engineer in the Navigation and Unmanned Aircraft Systems (UAS) Department at the MITRE Center for Advanced Aviation System Development in McLean, Virginia. In this role he supports multiple efforts to integrate civil and military unmanned aircraft into the National Airspace System (NAS). Dr. Cook currently cochairs the UAS Science and Research Panel (SARP). The SARP is responsible for identifying research gaps related to integrating UAS into the NAS, recommending solutions, and promoting alignment of research efforts across the government agencies represented in the UAS Executive Committee. Before joining MITRE, Dr.  Cook served as the UAS airworthiness certification lead at the Naval Air Systems Command and led the NATO Technical Working Group charged with developing an airworthiness standard for military fixed-​wing UAS. Dr. Cook earned his PhD in aerospace engineering from the University of Maryland in 2003, having previously earned an MS and a BS in aerospace engineering from North Carolina State University in 1993 and 1990, respectively. Graham Feasey is the Technical Director of Design Verification at General Atomics Aeronautical Systems, Inc. (GA-​ ASI). Feasey is the lead Mechanical Engineering Airworthiness Compliance Verification Engineer with authority in Propulsion Systems, Fuel System, Landing Gear, Fire Protection, and Mechanical Systems. Since 2005, he has worked in the field of unmanned aviation where he has worked on MQ-​1 Predator A, MQ-​ 1C Gray Eagle, MQ-​9A Predator B, and MQ-​9B Sky Guardian. In addition to Airworthiness, Feasey manages the Performance Simulation Team and the Material and Process Group.

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Feasey earned an Associate of Science in Chemistry from Saddleback Community College; a Bachelor of Science in Chemical Engineering from California State Polytechnic University, Pomona; and a Master of Science in Chemical Engineering from University of California San Diego. He is a member of the American Institute of Chemical Engineers. Tom Haritos is the Associate Director for Research/​UAS Research Program Manager at the Applied Aviation Research Center (AARC), Kansas State Polytechnic. Prior to assuming this position, Dr. Haritos served as a Professor and faculty researcher for the Department of Aeronautical Science at Embry-​Riddle Aeronautical University in Daytona Beach, Florida. Dr. Haritos was one of the pioneering faculty members to establish the unmanned aircraft systems (UAS) degree program at Embry-​Riddle. Dr.  Haritos’ research interests include UAS integration, UAS Security and Security Enabled Operations, UAS Tracking and Detection, UAS Operations, training and consulting, and much more. Dr. Haritos currently represents Kansas State at a national level committee as a voting member for the Critical Infrastructure Partnership Advisory Council (CIPAC) under the auspices of the Department of Homeland Security focused on UAS Security Operations and policy development for the protection of critical infrastructure from unauthorized UAS use. Additionally, Dr. Haritos is often involved with educational extension in terms of outreach curriculum development and integration. Dr.  Haritos holds a PhD  in computer and information sciences and a postgraduate Educational Specialist degree from Nova Southeastern University, a Master of Science Degree in Aeronautics with a safety/​management dual specialization from Embry-​Riddle Aeronautical University, and a Bachelor of Science Degree in Aviation Maintenance Management from Lewis University. Dr.  Haritos is also an FAA-​licensed Aircraft Maintenance Technician. Warren Jensen received his medical degree from the University of California, San Francisco. He was engaged in private practice for eight  years before his residency and board certification in aerospace medicine. Dr. Jensen joined the University of North Dakota in 1993, where he teaches human factors, aerospace physiology, and human performance aspects of aviation and space flight. He has served as the state air surgeon for the North Dakota Air National Guard, is an active pilot, and an FAA designated senior aviation medical examiner. He currently consults with the Customs and Border Protection Agency to provide human factors coursework for Predator operations. Saeed M.  Khan is a professor of engineering technology at Kansas State University Polytechnic. He earned his MS and PhD   in electrical engineering from the University of Connecticut and a BS  in electrical engineering from the Bangladesh University of Engineering and Technology. Dr. Khan’s specialization is in antennas and electromagnetic wave propagation. Dr. Khan has been working in stealth technology since he was a graduate student and on smart skins for radar evasive aircraft since 1994. Since 2008, Professor Khan has been active in various aspects of UAS research, which include sense-​and-​avoid systems, multipath mitigation for UAVs in urban canyons, radar cloaking materials, and wireless power transfer. He has more than 40 papers, book chapters, invention disclosures, and a patent in wireless power transfer. He has received over $3.5 million in funding as principal investigator (PI) or Co-​PI. Professor Khan was honored with the 2013–​2014 Rex MacArthur Family Fellow for demonstrating teaching excellence, commitment to research, and honorable service to the university. Gabriel B.  Ladd earned a bachelor’s degree in aerospace engineering from Boston University and a master’s degree in environmental science from the University of Maryland. He conducted his undergraduate research at NASA Goddard Space Flight

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Center, focusing on sensor design, testing, and building data for a UAS-​based collection system for CO2 and water vapor eddy correlation. Continuing at NASA Goddard while working on his master’s thesis research on UAS applications in precision agriculture, he collaborated with the AeroScience lab to design and build data collection and processing systems for small UAS as well as ground sampling equipment. After graduate school, Ladd worked at the Washington, DC area consulting firm Enegis, where he led the UAS consulting efforts and developed geospatial computer models to answer national-​level policy questions for the U.S. and foreign governments. He is currently an independent consultant, working with industry leading UAS operators to create camera systems and data workflows to help them collect, process, and analyze UAS data. Among his clients are industry leaders such as American Aerospace Technologies Inc. (AATI). Ladd’s work with AATI is pioneering applications, data workflows, and automation processes for data collected from Medium Altitude Long Endurance (MALE) UAS Beyond Line of Sight (BLOS) operations in the National Air Space (NAS). Robert D. Love is an aircraft conceptual design engineer at General Atomics Aeronautical Systems, Inc. (GA-​ ASI), focused on multidisciplinary UAS design optimization, life-​ cycle cost estimation, and mission analysis. He also runs AirMorph LLC to provide engineering consulting services. Previously, he worked as a project engineer for technology development, as an airframe design engineer at GA-​ASI, and supported F-​2 composites production at Lockheed Martin. He serves on the American Institute of Aeronautics and Astronautics (AIAA) Aircraft Design Technical Committee and has authored more than 12 reviewed papers and multiple patents. Dr. Love earned his PhD and MS in Aerospace Engineering at the University of Florida in 2011 after obtaining his Bachelor of Science in Aerospace & Materials Engineering from Auburn University. Nathan Maresch is a Kansas State University alumnus, graduating summa cum laude with a Bachelor of Science in electronic and computer engineering technology. Maresch holds private pilot and UAS remote pilot licenses, and has industry experience in industrial controls and automation. He served as an instructor for various electronics labs at K-​State Polytechnic before joining the Applied Aviation Research Center (AARC) full time in 2009 to begin research in avionics development and miniaturization. Subsequent research projects include a sense and avoid project, with the creation of a two-​dimensional obstacle avoidance system, a wireless power transmission project in conjunction with engineering technology faculty resulting in a patent granted, and an indoor UAS flight navigation research project. Maresch oversees maintenance operations and supervises maintenance staff at the AARC while researching new technologies, developing, integrating, and repairing unmanned aircraft electronics systems, and tuning control systems. He is deeply involved with RF components and performs comprehensive command and control analysis to predict link range performance prior to flight operations in new topography. Maresch also instructs students in advanced fixed-​wing UAS labs, from the simulator experience to real-​world advanced flight and field operations flights. Eric McClafferty is a partner and the head of the 35-​person International Trade Practice Group at the international law firm Kelley Drye & Warren LLP in Washington, DC. The group was recognized by Law 360 as U.S. International Trade Practice of the Year in 2013 and 2014. He helps universities, large and small companies, individuals, and industry associations comply with U.S.  regulations on international trade, particularly those relating to exports of unmanned systems. He earned a BA and an MA from the University of Michigan and JD from the University of Virginia.

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Benjamin Miller is the Director at Colorado Center of Excellence for Advanced Technology Aerial Fire Fighting. Ben began his public service career at Mesa County Sheriff’s Office (MCSO). Among many accomplishments in his 15  years with MCSO, Ben founded and served as program director of the first operational unmanned aerial system (UAS) program for a nonfederal public safety agency in the United States. The MCSO UAS program has been featured in prestigious print media—​such as National Geographic, Time, and Smithsonian Air & Space—​and has been covered by numerous news organizations around the world, including Fox News, CNN, MSNBC, NPR, The New York Times, and The Washington Post. Ben has more than 500 sUAS flight hours and 700 mission deployments. He has flown many types of unmanned operations, including flights over armed gunmen, crime scene reconstruction missions, search and rescue, and fire operations, all within the National Airspace System. Ben also had the opportunity to train game wardens in the African nation of Namibia in support of their antipoaching efforts. Throughout the industry, Ben is regarded as a thought leader on the applications of small-​unmanned aircraft in public safety. His perspective has been shared in presentations across the United States and Canada, including testimony to the United States Senate Judiciary Committee, as well as to other members of Congress at both the state and national levels. His expertise has been sought by prestigious organizations—​such as the National Aeronautics and Space Administration, the Federal Bureau of Investigation, the United States Secret Service, the United States Department of Justice, and the FAA’s NextGen Institute—​and numerous other entities in the unmanned aircraft systems space, including academia and industry. Over the last decade, Ben has had the great pleasure of serving on the Board of Directors for the Association of Unmanned Vehicle Systems International (AUVSI), as a voting member of ASTM’s committee F38 on Unmanned Aircraft Systems, and as a special advisor to the NIST response robotics working group. Ben has also held advisor positions on five start-​up UAS-​related companies and spent 2 years working for Draganfly Innovations who began building multicopters in 1999. Ben is now the Director of the Colorado Center of Excellence for Advanced Technology Aerial Firefighting. Ben’s team is the lead UAS organization for the Colorado Department of Public Safety. Focusing on the fire service, Ben’s team is also solving problems for public safety UAS users by creating solutions for real-​time geospatial intelligence sharing, counter-​UAS, management of airspace above wildland fires, and numerous other public safety-​related challenges. Michael T. Most, PhD, is an academic lead of the Unmanned Aircraft Systems Program at Kansas State University, prior to which he was an associate professor and chair of the Department of Aviation Technologies at Southern Illinois University. He has authored numerous articles for technical and refereed journals on aviation, aircraft design, and the use of GIS to investigate aviation-​related environmental externalities and delivered several peer-​reviewed papers on these same topics. Dr. Most holds FAA private pilot and A&P technician certificates, ASTM National Center for Aerospace and Transportation Technologies avionics certification, and a PhD in environmental resources and policy with an emphasis in remote sensing and geographic information systems. Rose Mooney is an executive director of the Mid-​Atlantic Aviation Partnership (MAAP), which is the FAA-​awarded UAS Test site in Virginia, New Jersey, and Maryland. Mooney’s career started with developing manufacturing robots, and then progressed to medical devices, wireless communications, and UAS. Prior to her current position, she spent over 30  years working in industry in engineering. Mooney’s interest in aviation began as a young child living in Queens NY in the approach path of LaGuardia Airport. She spent her nights watching the aircraft and being intrigued by flight. It seemed a natural fit when

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she began working in UAS. She earned her B.A.  in information systems management from Notre Dame University of Maryland after completing studies in computer science at University of Baltimore County. Randall K.  Nichols is Professor of Practice in Unmanned Aircraft Systems (UAS) –​ Cybersecurity at Kansas State University. Nichols serves as the Director, graduate UAS-​Cybersecurity Certificate program at KSUP. Nichols is author of nine textbooks, internationally respected, with 50 years of experience in leadership roles in cryptography, counterintelligence, INFOSEC, and sensitive computer applications. Nichols has provided counsel to the United States government. He is certified as a federal subject matter expert in both cryptography and computer forensics. He created five master and certificate programs for KSU and Utica College. To wit: • Author/​Developer: Certificate in Unmanned Aerial Systems (UAS) –​Cybersecurity • Retired Chair and Program Developer:  MS  –​Cybersecurity  –​Intelligence and Forensics • Retired Chair and Program Director: BS –​Cybersecurity and Information Assurance • Co-​Author/​Developer: MPS –​Risk Assessment and Cybersecurity Policy • Author/​Developer: MS Cyber Surveillance and Warfare Nichols served as COO of INFOSEC Technologies, LLC, a consulting firm specializing in Counterterrorism, Counterespionage, and Information Security Countermeasures to support its 1700 clients. Nichols served as CEO of COMSEC Solutions, a Cryptographic/​ Anti-​ virus/​ Biometrics Countermeasures Company, which was acquired by a public company in 2000. As part of the acquisition agreement, he served as the Vice President of Cryptography and the Director of Research. William H. Semke earned a PhD in mechanical engineering in 1999, an MS in engineering mechanics in 1993 at the University of Wisconsin-​Madison, and a BS in physics at Bemidji State University in 1991. He came to the University of North Dakota in 2000. He is currently a professor of mechanical engineering at the University of North Dakota where he conducts contemporary research in precision motion, vibration control, and aerospace hardware design, along with instruction in the areas of mechanical design and experimental methods. He also serves as the director of the Unmanned Aircraft Systems Engineering (UASE) Laboratory within the UND Center of Excellence for UAS Research and Education. Samuel Stewart is a senior electrical engineer and has spent his entire career at General Atomics Aeronautical Systems, Inc. (GA-​ ASI), since earning a Bachelor of Arts and Bachelor of Science degree in Electrical Engineering from the University of San Diego in 2004 (Summa Cum Laude). His primary role is a subject matter expert for the flight control computers, supporting and designing avionics for multiple unmanned aerial vehicle platforms. He also conducts production and field failure investigations for brushed and brushless servo drivers, airborne and ground communication networks, electrical power generation and distribution systems, and battery charging systems. Stewart excels in systematic troubleshooting of issues, and has authored a wide array of theory of operations documents, hardware to software interface control documents, failure mode effects analysis reports, component level requirements, design process documents, and system proposals. Michael Stroup is currently a Technical Director within the Advanced Airframe Engineering organization of General Atomics Aeronautical Systems, Inc. (GA-​ ASI).

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He has been with GA-​ASI since 1992. Recently, Stroup took roles conceptualizing UAS configurations, aerodynamic flow paths, and airfoil, flap, and slat designs. His duties range from external stores/​ pod shape creation, overall aircraft layout, and shaping, to Chief Engineer on subscales and small UASs. During Stroup’s 28  years, he has had substantial roles engineering 17 GA-​ASI aircraft, including exportable versions of the GNAT-​750, MQ-​1 Predator A, MQ-​9 Predator B, Predator C Avenger, MQ-​1C Grey Eagle, and many variations of those aircraft. Shortly, after achieving a Bachelor of Science degree in Mathematics from Fresno State University, Stroup began employment with Leading Systems, Inc., under Abe Karem, widely regarded as the “father of the UAV.” Mike has a passion for flying drones and giant scale RC models in his spare time. Brandon Suarez currently serves as the Technical Director for UAS Civil Airspace Integration for General Atomics Aeronautical Systems, Inc. (GA-​ASI), where he leads the company’s effort to integrate Remotely Piloted Aircraft Systems (RPAS) into domestic, foreign, and international airspace. Previously, at GA-​ASI, Suarez led a collaborative team of experts from the FAA, NASA, and several industry partners to bring together the technology needed for a Detect and Avoid (DAA) system on a Predator® B RPAS. He currently serves as the Co-​Chairman of RTCA Special Committee (SC-​228) and as an advisor to the ICAO RPAS Panel. Suarez is the author or coauthor of numerous journal and conference papers. He also served on the FAA’s UAS in Controlled Airspace Aviation Rulemaking Committee and as the Vice Chairman of NATO Industrial Advisory Group (NIAG) Study Group  205, which provided industry recommendations to the NATO specialist team standardizing DAA capabilities for military forces worldwide. Suarez holds both Bachelor’s and Master’s degrees in Aerospace Engineering from the Massachusetts Institute of Technology (MIT). He is also an instrument-​rated private pilot. Adam Trojanowski is an economic and policy analyst for the State of Colorado’s Center for Excellence Advanced Technology Aerial Firefighting, where he advises on complex policy and regulatory issues related to aerial firefighting, drones, and public safety aviation. Adam has attended a range of leadership schools and courses, including ones sponsored by the FBI, Colorado Department of Public Safety, and International Association of Chiefs of Police. He has also received specialized training in investigations, firearms, wildland firefighting, incident management, tactical casualty care and first aid, contract writing, pursuit driving, and defensive tactics and arrest control techniques. Adam is a life-​long public servant. He received a Juris Doctor degree from the University of Colorado Law School in 2004 and began his career serving the Honorable Morris B. Hoffman as a judicial law clerk. Adam coinstructed a practical civil procedure course for attorneys, assisted the judge with legal research, and drafted opinions. Adam earned a POST certificate from the State of Colorado after attending the Jefferson County Sheriff’s Office Police Academy, and served his community as a police officer and sergeant. In these roles, he trained law enforcement officers in patrol tactics, law, and firearms, and led a motorcycle patrol team. More recently, Adam developed state policies on public safety Unmanned Aerial Systems and has been working with the Colorado Attorney General’s Office and the United States Department of Justice on novel counter-​UAS questions. Adam is fluent in Polish and English, and holds a DHS Secret clearance. When not busy with public safety work, he enjoys skiing, cycling, reading about world history and human behavior, and supporting his children’s passion for dance and ballet. Benjamin Trapnell is an associate professor of aeronautics at the University of North Dakota. He holds a Bachelor of Science in physical sciences from the United States Naval

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Academy and a Master of Aeronautical Sciences degree from Embry-​Riddle Aeronautical University. He studied Aviation Safety Programs Management at the Naval Postgraduate School in Monterey, California, and has completed the Introduction to UAS Flight Test Short Course at the U.S. Naval Test Pilot School. Professor Trapnell has researched unmanned aircraft systems, sense and avoid technology, and the UAS regulatory environment for the Federal Aviation Administration. He led an interdisciplinary team that developed a Ganged Phased Array Radar risk mitigation system for the US Air Force. In his current assignment, he developed and implemented the first undergraduate Unmanned Aircraft Systems Operations degree program. Professor Trapnell has also developed and implemented the first undergraduate degree worldwide in unmanned aircraft systems operations. He has also worked closely with the FAA and the United States Air Force via Joint Unmanned Aircraft Systems Center of Excellence. Professor Trapnell served as a carrier aircraft plane commander and a flight instructor for the United States Navy. He holds commercial airplane and glider ratings and is a Life Member of the Academy of Model Aeronautics where he is a Leader Member as well as a Contest Director. Professor Trapnell serves as chairman of the Unmanned Aircraft Systems Committee for the University Aviation Association.

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1 History Douglas M. Marshall and Benjamin Trapnell

1.1 The Beginning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 The Need for Effective Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 The Radio and the Autopilot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 The Aerial Torpedo: The First Modern Unmanned Aircraft (March 6, 1918) . . . . . . . . 4 1.5 The Target Drone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.6 WWII U.S. Navy Assault Drone. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.7 WWII German V-​1 Buzz Bomb. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.8 WWII German Mistletoe. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.9 Early Unmanned Reconnaissance Aircraft. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.10 Radar Decoys: 1950s–​1970s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 1.11 Long-​Range Reconnaissance Unmanned Aircraft Systems: 1960s–​1970s . . . . . . . . . . 11 1.12 First Helicopter Unmanned Aircraft Systems: 1960s–​1970s. . . . . . . . . . . . . . . . . . . . . . 12 1.13 The Hunt for Autonomous Operation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.14 The Birth of the Twin Boom Pushers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.15 Desert Storm: 1991. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.16 Overcoming the Manned Pilot Bias. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.17 Amateur-​Built Unmanned Aircraft. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.18 Will Unmanned Aircraft Systems Replace Manned Aircraft?. . . . . . . . . . . . . . . . . . . . 16 Discussion Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1.1  The Beginning Long before the first manned powered flight took place (who accomplished this and when are still the subjects of much controversy), the history of the unmanned version of flying devices arguably begins with Homo sapiens’ curiosity about what makes birds fly, driven by a desire to overcome the limitations imposed by gravity. To soar with the birds, to observe the Earth from a point of view known only to the avian class of vertebrates, has been one of the strongest motivational forces in mankind’s history. Whether in mythology (Icarus, who flew too close to the Sun) or in the earliest texts from the High Renaissance, visionaries and dreamers (Da Vinci’s 1488 drawing of a flying machine) have provided glimpses of what might be possible and, in their own ways, laid down a road map for future generations to explore the potential for manned flight. Indeed, from centuries past when Chinese kites graced the skies, to the first hot air balloons in the mid-​18th century (the Montgolfier 1

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brothers), unmanned flying contrivances utilized the technologies of the day to pave a way forward for the development of manned aircraft. The technical revolution embodied in manned aircraft did not mean that unmanned aircraft were rendered obsolete. On the contrary, the modernization of systems developed for manned aircraft, coupled with advances in electronic systems, power sources, artificial intelligence, and nanotechnology, enabled the integration of automation that refined, if not defined, the capabilities of both manned and unmanned aircraft systems. In modern times, the term “unmanned aircraft” has come to mean an autonomous or remotely piloted vehicle that is used to navigate in the air. Even the name assigned to unoccupied aircraft has changed over the years, as viewed by aircraft manufacturers, civil aviation authorities, and the military. Aerial torpedoes, drones, pilotless vehicles, radio-​ controlled aircraft, remotely controlled aircraft, remotely piloted aircraft (RPA), autonomous aircraft, unmanned aerial vehicles (UAVs), unmanned aircraft systems (UAS) and others, are but a few of the terms used to describe a flying machine operated without a pilot onboard. In progressing through this chapter, the perceptive reader will observe that all aircraft, manned or unmanned, followed essentially the same iterative process involving the development of aerodynamic forces by wings or rotors that offset the weight of the craft, allowing it to fly. This progression involved the introduction of aircraft control, allowing the pilot to maneuver the aircraft in pitch and bank, effecting safe, aerodynamic control. When more than gliding was desired, the evolution of aircraft design included the creation of suitable propulsion systems, lightweight and powerful enough to drive the craft through the air. As the ability to fly greater distances became desirable, the need for proper navigational systems arose, and the resulting development of flight and navigation automation systems reduced the pilot workloads in flight. None of these innovations were trivial matters, as each relied upon the unique adaptation of immature existing technologies to create the new ones that were needed. Advancements in the sciences of aerodynamics, structures, propulsion, flight control systems, stabilization systems, navigation systems, communications and the integration of all in flight automation systems made the nearly parallel development of manned and unmanned aircraft systems possible. It continues today with refinements made feasible by the advancements in computer technologies and potential energy systems. In the early years of aviation, the idea of flying an aircraft with no one onboard had the obvious advantage of removing the risk to life and limb presented by these highly experimental contraptions.1 As a result, several mishaps are recorded where advances were made without injury to an onboard pilot. Although such approaches to remove people from the equation were used, the lack of a satisfactory method to affect control limited the use of these relatively primitive unmanned aircraft. Early aviation developmental efforts soon turned to the employment of the first “test pilots” to fly these groundbreaking craft. Further advances beyond unmanned gliders often had tragic results, as even pioneer Otto Lilienthal was killed flying an experimental glider in 1896. As seen with the current uses of unmanned aircraft, unmanned aircraft historically often followed a consistent operational pattern, described today as the “three Ds”, which stands for dangerous, dirty, and dull. Dangerous means that someone is either trying to bring down the aircraft or where the life of the pilot may be at undue risk operationally. Dirty is where the environment may be contaminated by chemical, biological, or radiological hazards precluding human exposure. Finally, dull is where the task requires long hours in the air, making manned flight fatiguing, stressful, and therefore, not desirable or safe for the pilot.

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1.2  The Need for Effective Control The Wright Brothers’ success in flying the first airplane is more of a technical success story in solving the problem of the pilot’s ability to control a heavier-​than-​air craft. Many aviation pioneers either used weight shifting to control their inventions or aerodynamic design (i.e., dihedral) to stabilize their craft, hoping that a solution would evolve during testing. Dr. Samuel P. Langley, the heavily government-​financed early airplane designer competing with those two bicycle mechanics from Ohio, also wrestled with the problem of how to control an airplane in flight. Dr. Langley’s attempts with a far more sophisticated and better powered airplane, however, ended up in the Potomac River; not once, but twice, over the issue of adequate flight control. After the Wright Brothers taught the fledgling aviation world the secrets of controlled flight, namely, their wing-​warping approach to roll control, and a movable horizontal “rudder” for pitch control, aircraft development experienced a burst of technical advancement. Yet, it took the tragedy of World War I (WWI) and the military demands of the 1914–​1918 conflict to stimulate the rapid development of a useful tool. All aspects of aircraft design, from relatively advanced power plants, to fuselage structures, to lifting wing configurations, and to control surface arrangements, began to mature into what we see today as the “airplane.” It was in the crucible of “the war to end all wars” that aviation came of age, and along with this wave of technological advancement came the critical but little recognized necessity of achieving effective flight control.

1.3  The Radio and the Autopilot As is often the case with many game-​changing technological advances, inventions of seemingly unrelated items combined in new arrangements to serve as the catalyst for new concepts. Such is the case with unmanned aircraft. Even before the first flight of Wright Brothers in 1903, the famous electrical/​mechanical engineer-​inventor and futurist Nikola Tesla proposed the idea of a remotely piloted aircraft in the late 1890s to act as a flying guided bomb. His concept appears to have been an outgrowth of his work building the world’s first guided underwater torpedoes, controlled by what was then called “teleautomation,” in 1898. Tesla preceded the invention of the radio in 1893 by demonstrating one of the first practical applications of a device known as a full-​spectrum spark-​gap transmitter. Tesla went on to help develop frequency separation and is recognized by many as the real inventor of the modern radio (Marconi’s advocates would disagree). While the electrical genius Tesla was busy designing the first electric architecture for the City of New  York, another inventor, Elmer Sperry, the founder of the famed flight control firm that still bears his name, was developing the first practical gyro-​control system. Sperry’s work, like Tesla’s, focused initially on underwater torpedoes for the Navy. He developed a three-​axis mechanical gyroscope system that took inputs from the gyros and converted them to simple magnetic signals, which, in turn, were used to affect actuators. The slow speeds of water travel, and weight not being as critical an issue for seacraft, allowed Sperry to perfect his design of the first reliable mechanical autopilot. Next, Sperry turned his attention to the growing new aircraft industry as a possible market for his maritime invention, not for the purpose of operating an aircraft unmanned, but as a safety

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device to help tame unstable manned aircraft, and to assist the pilot in maintaining his or her bearings in bad weather. Sperry began adopting his system of control on early aircraft with the help of airframe designer Glenn Curtiss. Together they made a perfect team of flyer-​designer and automation inventor. Following excellent prewar progress on the idea, the demand during WWI to find new weapons to combat Germany’s battleships combined the inventions of the radio, airplane, and mechanical autopilot to field the first practical unmanned aircraft, an aerial (or air-​dropped) torpedo.

1.4  The Aerial Torpedo: The First Modern Unmanned Aircraft (March 6, 1918) In late 1916, with war raging in Europe, the U.S. Navy, a military arm of a still neutral country, funded Sperry to begin developing an unmanned aerial or airborne torpedo. Elmer Sperry put together a team to tackle the most daunting aerospace endeavor of the time. The Navy contract directed Sperry to build a small, lightweight airplane that could launch itself without a pilot, fly out to 1,000 yards, guided to a target, and detonate its warhead at a point close enough to be effective against a warship. Considering that the airplane had just been invented 13 years earlier, the ability to even build an airframe capable of delivering a large explosive payload against an armored ship, a sizable radio with batteries, heavy electrical actuators, and a large mechanical three-​axis gyrostabilization unit, was by itself extraordinary, but then integrating these primitive technologies into an effective flight profile was spectacular. This aircraft was thus a direct ancestor of the modern cruise missile. Sperry tapped his son Lawrence to lead the flight testing conducted on Long Island, New York. As the United States entered WWI in mid-​1917, these various technologies were brought together to begin testing. It is a credit to the substantial funding provided by the U.S. Navy that the project was able to weather a long series of setbacks, crashes, and outright failures of the various components that were to make up the Curtiss-​Sperry N-​9 Aerial Torpedo. Everything that could go wrong did. Catapults failed; engines died; airframe after airframe crashed in stalls, rollovers, and crosswind shifts. The Sperry team persevered and finally on March 6, 1918, the Curtiss prototype successfully launched unmanned, flew its 1,000-​yard course in stable flight and dived on its target at the intended time and place, then recovered and landed. Thus was born the world’s first true unmanned system, or “drone.” Not to be outdone by the Navy, the Army invested in an aerial bomb concept similar to the aerial torpedo. This effort continued to leverage Sperry’s mechanical gyrostabilization technology and ran nearly concurrent with the Navy program. Charles Kettering designed a lightweight biplane that incorporated aerodynamic static stability features not emphasized on manned aircraft, such as exaggerated main wing dihedral, which increases an airplane’s roll stability at the price of complexity and some loss in maneuverability. The Ford Motor Company was tapped to design a new lightweight V4 engine that developed 41 hp and weighed 151 pounds. The landing gear had a very wide stance to reduce ground roll on landings. To further reduce cost and to highlight the disposable nature of the aircraft, the frame incorporated pasteboard and paper skin alongside traditional cloth. The aircraft employed a catapult system with a nonadjustable full throttle setting.

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FIGURE 1.1 U.S. Army Liberty Eagle (Kettering Bug).

The Kettering aerial bomb, dubbed the Bug (Figure 1.1), demonstrated impressive distance and altitude performance, having flown some tests at 100 miles distance and 10,000 ft altitudes. To prove the validity of the airframe components, a model was built with a manned cockpit so that a test pilot could fly the aircraft. Unlike the Navy aerial torpedo, which was never put into service production, the aerial bomb was the first mass-​produced unmanned aircraft. While too late to see combat in WWI, the aircraft served in testing roles for some 12–​18 months after the war. The aerial bomb had a supporter in the person of then Colonel Henry “Hap” Arnold, who later became a five-​star general in charge of the entire U.S. Army Air Forces in World War II (WWII). The program garnered significant attention when Secretary of War Newton Baker observed a test flight in October 1918. After the war, some 12 Bugs were used alongside several aerial torpedoes for continued test flights at Calstrom Field in Florida.

1.5  The Target Drone Surprisingly, most of the aviation efforts in unmanned aircraft after WWI did not pursue weapons platforms such as the wartime aerial torpedo and bomb. Instead, developers focused primarily on employing unmanned aircraft technology as target drones. In the interwar years (1919–​1939), the manned aircraft’s ability to influence the outcome of ground and naval warfare was recognized, and militaries around the world invested more in antiaircraft weaponry. This, in turn, created a demand for realistic targets, and the unmanned target drone was born. (Some have argued that target drones are the only true “drones.” All others are called UAS/​UAV/​RPAS etc., because they are intended to return home and perform missions other than being shot down). Target drones also played a key role in testing air war doctrine. The British Royal Air Force was in a debate with the Royal Navy over the ability of an airplane to sink a ship. In the early 1920s, General Billy Mitchell of the Army Air Corps sank a war prize German battleship and subsequent older target warships, to the dismay of the U.S. Navy. The counterargument to

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these demonstrations was that a fully manned ship armed with antiaircraft guns would easily shoot down attacking aircraft. The British used unmanned target drones flown over such armed warships to test the validity of the argument. In 1933, to the surprise of all, a target drone flew over 40 missions above Royal Navy warships armed with the latest antiaircraft guns without being shot down. Unmanned aircraft technology played a key role in formulating air power doctrine and provided key data that contributed to America, England, and Japan concluding that aircraft carriers, which played such a vital role in upcoming WWII, were a good investment. In the United States, the target drone effort was influenced by the development of the Sperry Messenger, a lightweight biplane built in both manned and unmanned versions as a courier for military applications, and as a possible torpedo carrier. Some 20 of these aircraft were ordered. The U.S. Army identified this class of aircraft in 1920 as a Messenger Aerial Torpedo (MAT). The effort waned in the early 1920s, however, and Sperry Aircraft Corporation withdrew from active unmanned aircraft design with the untimely death of Lawrence Sperry, the son of the founder and victim of an aircraft accident. As the U.S. Army lost interest in the MAT program, the service turned its attention to target drones. By 1933, Reginald Denny, a British-​born actor and an avid model aviation enthusiast, using an aircraft obtained from aviation modeler Walter Righter, perfected a radio-​controlled airplane only 10 ft long and powered by a single-​cylinder 8 hp engine. Having been an observer/​gunner in the Royal Flying Corps in WWI, Denney saw the possibility of creating a radio-​controlled target drone for the U.S. Army to allow gunners to actually shoot at an airborne target, known as the “Dennyplane.” With this aircraft, Denny won an Army contract and produced the target drone, as well as later models, in a shop located in Southern California. The Army designated this craft the OQ-​1 Radioplane, and subsequent versions continued with the OQ (subscale target) designation. The Navy bought the aircraft, designated Target Drone Denny 1 (TDD-​1). Some 15,000 of all variants were produced and they served throughout WWII as the world’s most popular target drone. The company was eventually sold to Northrop in 1952. In the late 1930s, the U.S. Navy returned to the unmanned aircraft arena with the development of the Navy Research Lab N2C-​2 Target Drone (Figure  1.2). This 2,500-​pound radial engine biplane was instrumental in identifying the deficiencies in Naval antiaircraft prowess. Much like the earlier Royal Air Force experience with the Royal Navy, where drones survived numerous passes on well-​armed warships, the U.S. Navy battleship Utah failed to shoot down any N2C-​2 drones that were making mock attacks on the ship. Curiously enough, the U.S. Navy added yet another title by describing this class of unmanned drones as No Live Operator Onboard (NOLO).2 During the same interwar years, the British Royal Navy attempted to develop an unmanned aerial torpedo and an unmanned target drone, both utilizing the same fuselage. Several attempts were made to launch these aircraft from ships, with little success. The Royal Aircraft Establishment (RAE) finally gained a measure of success with a “Long Range Gun with Lynx engine” called together the “Larynx.” This program was followed by the Royal Air Force automating an existing manned aircraft as its first practical target drone. This effort involved utilizing the Fairey Scout 111F manned aircraft converted as a gyrostabilized radio-​controlled plane, now referred to as the Queen. Of the five aircraft built, the first four crashed on their first flight. The fifth aircraft, however, proved more successful and succeeded in subsequent gunnery trials. The next evolution was to take the Fairey flight control system and combine it with the excellent, and highly stable, DeHavilland Gypsy Moth. Dubbed the Queen Bee, this aircraft

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FIGURE 1.2 Curtiss N2C-​2 target drone.

proved much more reliable than the earlier Queen, with the Royal Air Force placing an order for 420 target drones. This led to the designation of an unmanned aircraft being described by the letter Q to denote unmanned operation. This protocol was also adopted by the U.S. Military. Although unverified by research, the term drone is believed by some to have originated with the Queen name as meaning “a bee or drone.” Between WWI and WWII, almost all nations with an aviation industry embarked on some form of unmanned aircraft research. These efforts were mainly in the form of target drones.3

1.6  WWII U.S. Navy Assault Drone The U.S. Navy leveraged its experience with the 1930s N2C-​2 Target Drone, which was controlled by an operator from a nearby manned aircraft in flight, to develop a large-​scale aerial torpedo now reclassified as an assault drone. Initially, the assault drone effort took the form of the TDN-​1, built in a 200-​unit production run in early 1940. This aircraft had a wingspan of 48 ft and was powered by twin six-​cylinder O-​435 Lycoming engines with 220 hp each in a high-​wing configuration (Figure  1.3). The aircraft was intended to be employed as a bomb or torpedo carrier, in high-​risk environments, to mitigate the risk to aircrews. The groundbreaking advancement of this unmanned aircraft was the first use of a detection sensor in the form of a primitive 75-​pound RCA television (TV) camera in the nose to provide a remote pilot better terminal guidance from standoff distances. Given the relatively poor reliability and resolution of the first TVs, this was indeed a remarkable feat of new technology integration. The TDN-​1 was superseded by a more advanced model called the Navy/​Interstate TDR-​1 Assault Drone. Some 140 clones were built. A Special

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FIGURE 1.3 Naval aircraft factory TDN-​1 “Assault Drone.”

Air Task Force (SATFOR) was organized and sent to the Pacific Theater. It used the technology against the Japanese during the Bougainville Island Campaign in 1944 with limited, but definable, success. Operationally, a Navy Avenger Torpedo Bomber was flown as the guiding aircraft. Equipped with radio transmitters to affect radio control, a TV receiver was also installed that enabled an operator to guide the drone to its target from as far as 25 miles away. Approximately 50 aircraft were thus employed against various targets, attaining roughly a 33% success rate. The U.S. Navy and Army Air Forces then turned to outfitting older four-​engine bombers into unmanned aircraft to be deployed in the European Theater to destroy highly defended, high-​priority targets such as V-​1 Buzz Bomb bunkers in Siracourt and the heavily fortified U-​boat pens in Heligoland. Operation Aphrodite, as it was called, consisted of stripping out Navy PB4Y-​2 Privateers (the Navy version of the Consolidated B-​24) and B-​17s and packing them with high explosives. They were equipped with a Sperry-​designed, three-​ axis autopilot for stabilization, radio control links for remote control, and RCA TV cameras in the cockpit. The concept of the operation was for pilots in the aircraft to control it during takeoff. Once established in remote-​controlled cruising flights, these pilots would arm the explosives and parachute from the “flying bomb” over friendly England, while the aircraft, controlled by an operator in a nearby manned bomber, would be guided to its target. Operations commenced in August 1944, with rather dubious results. On the first mission, the aircraft spun out of control after the pilots left the plane. Subsequent flights ended similarly with loss of control of the aircraft or lack of suitable visibility to fly the aircraft accurately to the target. On August 12, 1944, a BQ-​8 “robot” (converted B-​24 Liberator) aircraft with two pilots at the controls detonated prematurely, killing Navy Lieutenants Wilford J. Wiley and Joseph P. Kennedy. The latter was President John F. Kennedy’s older brother and son of the former U.S. Ambassador to England, Joseph Kennedy. Continued failures with equipment and/​or operational weather-​related incidents, combined with the rapid advancement of Allied forces in Europe, forced the cancellation of the program. In retrospect, this could be considered the first use of unmanned aircraft as an offensive weapon.

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1.7  WWII German V-​1 Buzz Bomb The most significant unmanned aircraft of WWII was Nazi Germany’s V-​1 Buzz Bomb (Vengeance Weapon-​1). Based on the earlier 1930s work by inventor Paul Schmidt in developing a practical pulse jet, the aircraft integrated an advanced, lightweight, and reliable three-​axis gyrostabilized autopilot, a radio signal baseline system for accurate launch point data, and a robust steel fuselage that was resistant to battle damage. The V-​1 represented the first successful, mass-​produced, cruise-​missile-​type unmanned aircraft, and its configuration influenced many postwar follow-​on unmanned aircraft designs (Figure 1.4). The V-​1 was manufactured by Fieseler Aircraft Company in large numbers, with more than 25,000 built. This high number makes the V-​1 the most numerous combat unmanned aircraft in history, excluding modern hand-​launched platforms. The aircraft was flexible in being capable of both ground and air-​launching. It utilized a powerful pneumatic catapult system, which is a familiar feature on many modern-​day UAS. The pulse jet was a simple, lightweight, high-​thrust device that operated on the principle of cyclic compressions/​ explosions at about 50 times per second. Employing closing veins to direct the gas toward the exhaust, these cycles created the hallmark “buzz” sound made by the engines in flight. Although not fuel efficient by traditional jet engine standards, the pulse jet was inexpensive to produce, provided high thrust, was reliable, and could operate with significant battle damage. The V-​1 was also the world’s first jet-​powered unmanned aircraft, weighing about 5,000 pounds, with an impressive 1,800-​pound warhead. Operationally, the V-​1 was primarily employed from ground-​launch rail systems. A small number were air-​launched from Heinkel 111 bombers, making the V-​1 the world’s first air-​ launched unmanned aircraft as well. Some 10,000 V-​1s were launched against Allied cities and military targets, killing some 7,000 people. Though the V-​1’s guidance system allowed it to maintain heading and altitude, it was unable to provide the capability of in-​flight navigation. Accurate weather forecasts, primarily wind direction and speed, were necessary to allow the operators to launch the aircraft in the right direction. At a predetermined time in the flight, a device would close the

FIGURE 1.4 Fieseler FI 103 (V-​1) German Buzz Bomb.

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fuel valve, thereby terminating powered flight. The aircraft would then assume a nose-​ down attitude, with the warhead detonating on contact with the ground. It was necessary therefore to have accurate intelligence to determine where these bombs landed so that proper preflight preparation would allow for some modicum of accuracy. Though a meager 25% were considered successful, when compared to its fairly low cost, and the reportedly devastating effect it had on public morale, the V-​1 was considered to be an effective weaponized unmanned aircraft. Mass produced, and employing many firsts for autonomously flown aircraft, it influenced future designs and provided the historical context to fund many more sophisticated unmanned programs during the following Cold War.4 The U.S. Navy built a reverse-​engineered copy for use in the invasion of Japan and launched improved versions from submarines on the surface, gaining yet another title as the world’s first naval-​launched, jet-​powered, unmanned cruise missile.

1.8  WWII German Mistletoe The teaming of manned and unmanned aircraft was not the exclusive domain of the Allies in WWII. In addition to the V-​1, the Germans, built a significant number of piggyback aircraft configurations known as Mistletoe Bombers. The main issue with the effectiveness of the V-​1 was that it was not very accurate in flying to its desired target. Mistel (Mistletoe) was an attempt by the Germans to deal with this problem. The concept was for an unmanned bomber, usually a twin-​engine JU-​88, being modified to carry a manned fighter, supported by struts, on its upper surface. The pilot of the manned fighter would guide the bomber to its target, and then, release it allowing an onboard stabilization system to allow the explosive-​laden bomber to glide to the target. Though about 250 such examples were built, the concept had marginal success, due primarily to operational challenges rather than technical issues. The German Mistletoe concept could be termed more of a guided bomb than an unmanned aircraft, and several gliding guided bombs were developed by the Germans with limited success. The lines between guided missile and unmanned aircraft are not always clear, and in WWII, the V-​1 assault drones, explosive-​packed, radio-​controlled bombers, and the piggyback Mistletoe configuration all involved forms of an airplane, which places them in the category of unmanned aircraft. This distinction is far less clear in the view of future cruise missiles, which are more closely related to their ballistic cousins than airplanes.

1.9  Early Unmanned Reconnaissance Aircraft As we have seen from the beginning of the first successful unmanned aircraft flight in 1918 to WWII, unmanned aircraft have been employed mainly in the target drone and weapons delivery roles. Unmanned aircraft development in the follow-​on Cold War years shifted dramatically toward reconnaissance and decoy missions. This trend has continued today, where nearly 90% of unmanned aircraft are involved in some form of data gathering in military, law enforcement, environmental monitoring and aerial inspections applications, and

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more recently, commercial payload delivery.. The main reasons unmanned aircraft were not employed in WWII for reconnaissance had more to do with the imagery technology and navigation requirements than the aircraft platforms themselves. Cameras in the 1940s required relatively accurate navigation to gain the desired areas of interest, and navigation technology of the day could not compete with a trained pilot with a map. This changed in the postwar years with the advent of radar mapping, better radio navigation, Loran-​type networks, and global and inertial navigation systems, all enabling an unmanned aircraft to fly autonomously to and from the target area with sufficient accuracy. One of the first reconnaissance high-​performance unmanned aircraft to be evaluated was the Radio Plane YQ-​1B high-​altitude target drone modified to carry cameras, subsequently GAM-​67. This turbojet-​powered aircraft was primarily air launched from B-​47 aircraft and was proposed to be used in the suppression of enemy antiaircraft destruction (SEAD) role. Cameras were also proposed, but the program was canceled after only about 20 aircraft were built. Poor range and high cost were given as the reasons for cancellation.

1.10  Radar Decoys: 1950s–​1970s The Vietnam War of the 1960s and early 1970s created a high demand for countermeasures to Soviet-​built surface-​to-​air missiles (SAMs) used by the North Vietnamese. The missile threat relied extensively on radar detection of American aircraft. Jamming of these radars was attempted with mixed results. However, even under the best of circumstances, jamming ground-​based radars with airborne systems was problematic in that the ground system probably had access to more power, enabling the radar to overcome the jamming emitter. A more effective solution was to fool the radar into believing that it has locked on to a real aircraft and having it waste its expensive missiles on a false target. The U.S. Air Force embarked on such a solution by developing a series of unmanned aircraft to decoy enemy SAM batteries. To deceive a radar operator into believing a decoy resembles a B-​52 Bomber, for example, the decoy aircraft does not need to be built to physically resemble the actual aircraft. Only minor radar reflectors are needed to create a return radar signal that mimics the intended target. The addition of radios that mimic the electronic signatures of such aircraft enhances the illusion. As a result, the unmanned Air Force decoys were small in size but had the desired effect. The most frequently used radar decoy was the McDonnell ADM-​20 Quail, which could be carried inside the bomb bay of a B-​52 and air launched prior to the bombing run. The Quail weighed about 1,000 pounds, had a range of 400 miles, and could mimic the speed and maneuvers of a B-​52. As radar resolution improved, the decoys became less effective and most were out of service by the 1970s.

1.11  Long-​Range Reconnaissance Unmanned Aircraft Systems: 1960s–​1970s The U.S. Air Force pioneered the first mass-​produced, long-​range, high-​speed unmanned aircraft designed to conduct primarily reconnaissance missions, but these systems evolved

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FIGURE 1.5 AQM Lightning Bug.

into supporting a wide array of tasks, from suppression of enemy air defenses to weapons delivery. The Ryan model 147, later renamed the AQM-​34 Lightning Bug and Firebee series, has the longest service record for an unmanned aircraft. Designed as an initiative of the Ryan Aircraft Company in the late 1950s from an earlier target drone, the aircraft was powered by a turbojet, employed low drag wing and fuselage configuration and could reach altitudes in excess of 50,000 ft and speeds of 600 knots (high subsonic). The “Bug,” as it was called by its operators, had a long career and flew in a wide range of highland low-​ altitude profiles performing electronic signal-​ gathering intelligence, camera reconnaissance, and various decoy radar signal transmissions. A frequent violator of hostile airspace, many were shot down, but sufficient numbers successfully completed their missions to justify their use. The aircraft underwent many modifications over its operational life spanning the early 1960s to 2003. Many unique and groundbreaking technologies were employed in the Bug unmanned aircraft, including air launch from the wing store of modified DC-​130 aircraft to midair parachute snag recovery from H-​ 2 “Jolly Green Giant” helicopters. The AQM-​34 performed high-​priority missions of great national importance, such as reconnaissance missions during the 1960s Cuban Missile Crisis, to relatively mundane tasks as a target drone for fighter aircraft air-​to-​air missiles (Figure 1.5).

1.12  First Helicopter Unmanned Aircraft Systems: 1960s–​1970s The U.S. Navy’s QH-​50 Drone Anti-​Submarine Helicopter (DASH), developed in the early 1960s, established several firsts for unmanned aircraft. This unusual stacked, counter-​ rotating rotary wing aircraft was the first unmanned helicopter and the first unmanned aircraft to take off and land on a ship at sea. The requirement for the DASH was to extend the delivery range of antisubmarine homing torpedoes. A typical destroyer in the early 1960s

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could detect a submarine at ranges of over 20 miles, but could only launch weapons at less than 5 miles from the target. This small, compact, unmanned helicopter only needed to fly off to the maximum detection range and drop its homing torpedoes over the submerged submarine. The QH-​50 DASH used remote control via a pilot on the fantail of a ship to take off and land, and then employed a gyrostabilizer autopilot to direct the aircraft to a location that was tracked by the launching ship’s radar. More than 700 DASH aircraft were built and were used from 1960s to the mid-​1970s, when they finished up their career as towing targets for antiaircraft gunnery. Several countries, including France and Japan, operated the DASH aircraft.

1.13  The Hunt for Autonomous Operation From the very first unmanned aircraft, designers strived to gain as much independent flight operation from manned ground control as possible. Military requirements called for maximum standoff distance, long endurance, and significant data streams from onboard sensors. The demand for data competed with bandwidth for flight control transmission, further driving the need for self-​flight or autonomous operation. Enemy jamming may delay sensor transmission, but disrupting required flight control information might cause the loss of the aircraft. Cognizant of Britain’s ability to jam its signals, the German V-​1 Flying Bomb of WWII employed a crude, fully autonomous flight control and navigation system based on mechanical gyros, timers, and some primitive preprogramming involving fuel shutoff to initiate the termination dive. It was not until the advent of small, lightweight digital computers, inertial navigation technology, and finally the global positioning system (GPS) satellite network, that autonomous unmanned aircraft operation gained flight autonomy on a par with a human-​piloted vehicle. Lightweight computer technology developed in the 1970s, which led to the worldwide explosion in personal computers and the digitalization of everyday items from wristwatches to kitchen appliances, played the most significant role in unmanned aircraft autonomy. With each advance in computing power and cache memory retrieval, unmanned aircraft gained greater flexibility in addressing changes in winds and weather conditions as well as new variables affecting the mission equipment payloads. Mapping data could now be stored aboard the aircraft, which not only improved navigation but also enabled more accurate sensor camera imagery.

1.14  The Birth of the Twin Boom Pushers The U.S. Marine Corps’ groundbreaking work in the late 1960s with the “Bikini” drone, built by Republic Aviation, laid the foundation of what was to become the most popular UAS configuration, the AAI RQ-​7 Shadow, which is the most widely deployed military UAS outside of the hand-​launched AeroVironment RQ-​11 Raven. The Bikini drone, which did not receive any official military designation, was a small, lightweight aircraft, with a fuselage focused on providing the camera payload with a nearly unobstructed field of view attained by placement in the nose section. This evolved into a pusher engine arrangement

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further simplified by a twin boom tail (such as the RQ-​7 Shadow). Although “delta pusher” designs were attempted, most notably the Lockheed MQM-​105 Aquila UAS, this aerodynamic configuration made weight and balance a more challenging proposition, since the elevator moment arms were generally fixed, whereas the twin booms could be easily extended. In the late 1970s, capitalizing on the Marine Corps Bikini configuration, the Israelis developed a small tactical battlefield surveillance UAS called the “Scout,” built by Israel Aerospace Industries (IAI). The Scout was accompanied by IAI Decoy UAV-​A and the Ryan-​built Mabat. The decoys were designed to be flown against SAM batteries to fool their radar into activating early or even firing a missile on the drone itself. The Mabat was designed to collect antiaircraft radar signals associated with SAM batteries. Finally, the Scout was designed to exploit the actions of the other two in order to put eyes on the SAM batteries for targeting information and damage assessment after a strike. In addition, the Scout provided close-​up battlefield imagery to maneuvering ground commanders, a first for unmanned aircraft. This approach differed greatly from all the previous reconnaissance UAS platforms, in that their imagery was more operational and strategic, with film being developed afterward or even electronically transmitted to a collection center for analysis. The advances in small-​sized computers enabled this real-​time bird’s-​eye view to a maneuvering leader on the ground directly influenced the decision process on small groups of soldiers or even individual tank movement. Israeli forces made significant advances in battlefield situational awareness during the June 1982 Bekaa Valley conflict between Israeli and Syrian forces. “Operation Peace for Galilee,” as it was called by Israel, involved an Israeli ground offensive against Hezbollah terrorists occupying southern Lebanon. Syria, allied with Hezbollah, occupied a large portion of the Bekaa Valley with a sizable ground force consisting of large numbers of new Soviet tanks and heavy artillery. Syrian forces were supported by sophisticated Soviet-​ built SAM batteries. Israel used a combination of jet-​powered decoys and Mabat signal-​ gathering UASs to detect and identify the Syria SAM battery-​operating frequencies, and then using the Scout with other manned assets quickly destroyed most of the SAM threat, enabling the Israeli ground forces to maneuver with close air support. The Scout UAS, with its twin boom pusher configuration, flew along the sand dunes of the Bekaa Valley and identified Syrian tanks with near real-​time data feed to maneuvering Israeli small-​unit commanders. This eye-​in-​the-​sky advantage enabled a smaller force to move with greater speed, provided excellent targeting data to Cobra attack helicopters, and directed very effective artillery fire. The Scout UAS was too small to be picked up and tracked by Syrian Soviet-​designed radar and proved to be too difficult to observe by fast-​moving Syrian jet fighters. The 1982 Bekaa Valley experience initiated a worldwide race to develop close-​ battle unmanned aircraft.

1.15  Desert Storm: 1991 While the short 1982 Israeli–​Syrian Bekaa Valley campaign represented the first use of close battle UASs, Desert Storm in 1991 represented the first wide-​scale deployment of those systems. The United States and its allies used unmanned aircraft continuously from Desert Shield through Desert Storm. The most frequently employed system was the now-​familiar twin boom pusher configuration of the AeroVironment FQM-​151 “Pointer” and the AAI

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FIGURE 1.6 AAI RQ2 Pioneer.

RQ-​2 “Pioneer” (Figure 1.6). The Pioneer aircraft, essentially an upgraded “Scout,” was a joint Israeli–​U.S. effort that was powered by a 27 hp snowmobile engine, flew via a remote control joystick on the ground, had a range of about 100 miles, and required an altitude of 2,000 ft to maintain a line-​of-​sight transmission data link. Fully autonomous flight was technically possible, but operators of these aircraft opted to have a manned pilot remotely piloting the aircraft to achieve more responsive battlefield maneuvering at a desired point of interest. GPS and computer power were not yet sufficiently integrated to enable ground operators to simply designate waypoints on short notice. Also, imagery feeds via satellite links were not sufficiently developed at that small dimension to affect transmission of data. During Desert Storm, U.S.  forces flew some 500 UAS sorties. The Pointer and Pioneers guided artillery, even directing the heavy 16-​inch gunfire from the battleship Iowa. There is a documented case in which a group of Iraqi soldiers attempted to surrender to a Pointer flying low over the desert. After the Desert Storm experience most militaries around the world concluded that UAS platforms did indeed have a role to play in spotting enemy locations and directing artillery fire. Conversely, many military analysts concluded that the vulnerable data links precluded UAS use across the board as a replacement for most manned aircraft missions and roles. This opinion was based in part on the limitations of the line-​of-​sight data link of the Pointer and Pioneer, and a deep-​seated cultural opposition from manned aircraft pilots and their leadership. A large segment of a nation’s defense budget is dedicated to the procurement of military aircraft and the training and employment of large numbers of pilots, navigators, and other crew members. Most air forces choose their senior leaders after years of having proved themselves in the cockpit flying tactical aircraft. The very idea of cheaper, unmanned aircraft replacing manned platforms ran against what President Eisenhower warned as the self-​fulfilling “military–​industrial complex.”

1.16  Overcoming the Manned Pilot Bias From the 1990s to the terrorist attacks of 9/​11, unmanned aircraft made slow progress, leveraging the increases in small, compact, low-​cost computers and the miniaturization

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of a more accurate GPS signal. However, the barrier to widespread acceptance laid with manned aircraft platforms and the pilots who saw UAS technology as replacing their livelihoods. When 9/​11 occurred, the U.S. Army had only 30 unmanned aircraft. By 2010, that number grew to more than 2,000. The argument against unmanned aircraft had finally given way to the low cost, the reduced risk, and the practicality of a drone, as the press, the regulators and the general public still call them today, performing the long, boring missions of countless hours of surveillance in both Iraq and Afghanistan. With a person still in the loop of any lethal missile leaving the rails of an Air Force Predator UAS, the “responsibility” argument has for the time being been addressed.

1.17  Amateur-​Built Unmanned Aircraft As mentioned earlier in this chapter, Nikola Tesla pioneered the development of a means for successfully controlling an object from a remote location. It should not be difficult, then to see from a military standpoint that such a device would have a significant impact on the nature of armed conflict. It should also be obvious that inquisitive amateurs would also be interested in investigating the possibilities of utilizing this technology for controlling model aircraft that had thus far been flown by line control or as free-​flying models. In the 1930s, the British developed the “Queen Bee” remotely piloted aerial drone. Reginald Denny, the British actor and avid aeromodeler, like many enthusiasts who look to make a career out of their hobby, used his aircraft modeling passion, and a desire to incorporate the new methods for radio control, to develop aircraft to be sold to the government as target drones. The control systems of the time were large, heavy, and very crude compared to modern radio-​control (RC) systems. Proportional control, the idea of providing incremental flight control displacement that matches what a pilot could do in the cockpit, was but a dream to the early RC pilots. But, as with many dreams, persistence on the part of passionate pioneers, the development of faster and cheaper computer technologies, the creation of microelectric mechanical systems (MEMS), GPS navigation on a computer chip, miniature power plants, and advanced radio systems, have created an environment conducive to a rapid transformation of a toy into a viable tool. It can be argued that the rapid advancements in the hobbyist radio-​controlled aircraft and the development of miniature automated stability and navigation systems are creating the commercial revolution of a technology that is rivaled only by computers and the mobile phone. The revolution has happened so quickly that regulatory entities such as the Federal Aviation Administration of the United States have had difficulty controlling its proliferation in many commercial industries. Predictably, the “military industrial complex” presses for the development of unmanned aircraft from one side, while the hobbyists and legions of entrepreneurs push for the commercial use of unmanned aircraft from the other.

1.18  Will Unmanned Aircraft Systems Replace Manned Aircraft? The spectrum of unmanned aircraft control runs from a completely autonomous flight control system independent of any outside signals to one that employs a constant data

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link enabling a pilot to remotely fly the aircraft with, of course, any number of variations in between. A fully autonomous aircraft could, in theory, fly without the effects of signal jamming and carry out a variety of complex missions. The disadvantage is that a fully autonomous flight control system can be simulated in a computer, enabling a hostile actor to develop counters to the system much in the same way that video gamers do with autonomous opponents. Once the program flaws are identified, defeating the autonomous system becomes a simpler task. Additionally, fully autonomous systems would most likely not be allowed to employ lethal force in a military or law enforcement setting, since the chain of responsibility is nearly nonexistent (someone has to initiate the mission, but after that all bets are off). At the other end of the spectrum, an aircraft that depends on an outside signal, no matter how well it is encrypted, has the potential to be jammed, or worse, directed by the enemy through a false coded message. Even if true artificial intelligence is developed enabling an unmanned aircraft to act autonomously with the intuitiveness of a human being, the responsibility factor will prevent the UAS from fully replacing manned aircraft. This is even more true with civil applications of passenger travel where at least one “conductor” on board will be required to be held accountable for the actions of the aircraft and to exercise authority over the passengers. A truly autonomous passenger aircraft, one without a pilot on board or “in the loop” is now science fiction, but as computers become more capable and other improvements are made in the fields of structural dynamics, airspace management, and human-​machine interfaces, it would be foolish to declare that such a concept will never happen. It becomes clear, then, that all of the limitations and risks that are manifest in military applications of unmanned systems are no less significant when applied to non-​military, law enforcement, governmental, civilian, and commercial uses of autonomous or semi-​ autonomous UAS. Hacking, misdirection, spoofing, system disruptions or failures, operator error, and a multitude of other potential mishaps are all factors that must be addressed by regulators, developers, manufacturers, end users and recipients of services when contemplating the substitution of UAS for other forms of consumer services. These challenges and issues are the subjects of the following chapters in this book.

Discussion Questions 1.1 Sir George Caley, Otto Lilienthal, Samuel Pierpont Langley, and the Wright Brothers: All used unmanned aircraft in the development of their concepts of manned aircraft. What might their reasoning have been and what might some of the advantages have been in so doing? 1.2 Some have argued that the Wright Brothers’ greatest accomplishment was figuring out how to provide a method to control an aircraft about the lateral axis. The Kettering Bug did not use wing warping or Curtiss’ ailerons for such control. How might this have facilitated or hindered the early unmanned aircraft’s success? 1.3 Replacing the human in the manned aircraft with a suitable mechanical system proved to be a daunting problem in the past, and even, in some ways, to this day. How did the efforts of Tesla and Sperry serve to pave the way to an automated system? 1.4 What was the most significant unmanned aircraft of WWII? What influenced your choice?

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1.5 A lack of accuracy in automated navigation systems prevented nearly all militarized unmanned aircraft from being effective strategic weapons prior to the Gulf War. What technological advancements changed this situation? 1.6 Many can point to myriad reasons that allowed the successful development of UAS. Which technologies played a major role in the development of commercial UASs? What technology advancements will provide the next great leap in the capabilities of UAS?

Notes 1 The German aviation pioneer Otto Lilienthal, circa 1890s, employed unmanned gliders as experimental test beds for main lifting wing designs and the development of lightweight aerostructures. So, too, did the Wright Brothers, flying their first gliders as kites, to unlock the mathematics of lift and drag, and working out the details of aircraft control, all the while remaining safely on the ground. 2 The Navy target drone program of the late 1930s developed the technique of controlling an unmanned aircraft from a manned aircraft in flight. Used with some success during WWII, the technique was rediscovered and used in Vietnam and too [no! “to” is the correct word]much greater effect in the Iraq conflict. 3 Germany, however, was an exception. Paul Schmidt, who pioneered the pulse jet as a low-​cost, simple, high-​performance thrusting device in 1935, found his work being considered by Luftwaffe General Erhard Milch, who recommended the new pulse jet be adapted to unmanned aircraft, which later took the form of the Fieseler Fi 103 flying bomb; better known to the Allies as the “Buzz Bomb.” 4 The operational capabilities of the V-​1 and its ability to carry a large weapon across international borders unmanned are [should probably be “were”] likely the impetus for the inclusion of the following statement in the initial charter of the International Civil Aviation Organization’s Chicago Convention of 1944: “No aircraft capable of being flown without a pilot shall be flown without a pilot over the territory of a contracting State without special authorization by that State and in accordance with the terms of such authorization. Each contracting State undertakes to insure that the flight of such aircraft without a pilot in regions open to civil aircraft shall be so controlled as to obviate danger to civil aircraft.” Convention on International Civil Aviation, Art. 8. Chicago, 1944.

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2 UAS Applications Mark Patrick Collins

2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2 Basic Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2.1 Control Methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2.1.1 Manual Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2.1.2 Stabilized Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2.1.3 Automated Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3 Payloads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.1 Remote Sensing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.2 Passive Electro-​Optical Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.3.2.1 Electro-​Optical Imaging System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.2.2 Visible RGB Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.2.3 Full-​Motion Video Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.2.4 IR/​NIR/​SWIR Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.2.5 MWIR/​LWIR Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.3 Active Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.3.1 LiDAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.3.2 Radar and Synthetic Aperture Radar. . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4 UAS Software for Commercial Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.1 UAS Fleet Management Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.4.2 Autopilot Software. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.3 Sensor Data Asset Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.4 Analytical Photogrammetry Software. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.4.5 Change Detection and Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.4.6 Computer Vision. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.4.6.1 Autonomous Flight Path Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.5 Commercial Applications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5.1 Building and Roof Inspections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5.2 Aircraft Inspections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.5.3 Oil, Gas, Power Lines, and Nuclear Power Plants . . . . . . . . . . . . . . . . . . . . . . . 34 2.5.4 Industrial Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.5.5 Civil Infrastructure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.5.6 Electric Power Industry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.5.7 Wind Turbine Inspection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.5.8 Tower/​Antenna Inspection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.5.9 Oil and Gas Inspection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.5.10 Photogrammetric Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 19

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2.5.11 Aerial Mapping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.12 Aerial Surveying. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.13 Volumetrics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.14 Precision Agriculture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.15 Natural Resource Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.16 Aerial Filming and Photography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.17 Filmmaking. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.18 Real Estate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.19 Marketing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.20 News Reporting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.21 Intelligence, Surveillance, Reconnaissance, and Emergency Response. . . . . . 2.5.22 Law Enforcement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.23 Search and Rescue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.24 Signals Intelligence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.25 Communications Relay. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.26 Atmospheric Information Collection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.27 Meteorology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.28 Hazardous Material Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.29 Radioactive Material Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.30 Applications Requiring Physical Interaction with Substances, Materials, or Objects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.31 Aerial Chemical Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.32 Water Sampling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.33 Small Unmanned Cargo Aircraft Delivery. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.34 Large Unmanned Cargo Delivery. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Additional Considerations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Mission Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.2 Data Processing and Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

39 40 41 41 43 43 43 44 44 44 45 45 45 46 47 47 47 48 48 48 48 49 49 50 51 51 52 52 52

2.1  Introduction Unmanned technology, and robotics in general, is a revolutionary technology that has applications in nearly every industry. There are new unmanned systems applications discovered on a daily basis that can improve the efficiency or safety of countless tasks. This chapter will provide an overview of some of the most common applications for unmanned aircraft systems (UAS) at various stages of maturity. Additionally, the basic technology that differentiates applications and operational considerations will be explored. The UAS applications range from simple video capture to precise scientific measurement. The knowledge UAS operators need to complete their missions varies dramatically from very few prerequisites to extensive specialized training in a scientific discipline. This range in expertise is a clear demonstration that unmanned aerial vehicles (UAVs) are tools used to collect specific information for a particular application. Outside of the flight training environment, the operation of an unmanned system is rarely conducted solely for

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the purpose of flying an aircraft. Therefore, it is critical to know the purpose of the mission and the type of data collection required prior to the actual launch of the UAS. The applications discussed in this chapter all have their own set of challenges and variables. The best practices associated with each application could cover an entire book. This chapter provides only a broad cross section of the industries and missions.

2.2  Basic Technology Before launching into a discussion of the many and varied uses for UASs, some foundational information on methods of vehicle control, stabilization, and sensor design may prove beneficial, particularly for those with limited exposure to unmanned technology. The topics included in the next few subsections will provide a fundamental understanding of basic platform control and sensor technology. 2.2.1  Control Methods To begin the discussion, the various methods of aircraft control and methods of conducting missions must be considered. The operation of the aircraft ranges from full manual control to stabilized or “remote control” and to automated flight profiles without direct flight path control. The level of automation in the flight mission is dependent upon several factors that include, but are not limited to, the amount of repetitious aircraft movement required, the aircraft’s proximity to other objects, and the dynamic nature of the mission. 2.2.1.1  Manual Control Under manual control the operator has direct, unassisted control of the aircraft’s flight path. The control input is typically applied through a handheld console that allows the operator to make fine changes in aircraft pitch, roll, yaw, and throttle (see Figure  2.1). The console can be configured to provide exponential control depending on the degree of input applied. Fine adjustments can be made with small inputs and large inputs can cause exponentially larger effects. The operator may also have direct control over other aircraft subsystems such as flaps, landing gear, and brakes. Manual aircraft control provides a skilled operator with precise control over the aircraft’s flight path and predictable outcomes to control inputs. However, safe and effective manual control requires extensive operator training and experience. Because of the difficulty of manually controlling an aircraft, many operators have spent a lifetime developing the flying skills they need to be capable of full manual control. 2.2.1.2  Stabilized Control Under stabilized control, the operator has direct, assisted control of the aircraft’s flight path. This type of aircraft control typically routes the operator’s inputs from a handheld console through an autopilot onboard the aircraft that translates the direct inputs into desired outputs. Stabilized control allows the operator to maintain control of the aircraft’s position, but reduces the fine control needed to ensure that a fixed-​wing aircraft returns to

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FIGURE 2.1 Example of an external pilot (EP) console that can be used to manually control the aircraft. (Courtesy of Kansas State University Polytechnic.)

wings level or a vertical take-​off and landing (VTOL) aircraft returns to hover. Some VTOL aircraft are equipped with a magnetometer that designates a single direction as “away” from the operator so that away, left, right, and toward the operator remains constant, regardless of the aircraft’s orientation. Stabilized control greatly reduces the level of skill required for the operator to effectively and safely control the aircraft while still providing dynamic control of the flight path. Most VTOL systems are capable of stabilized control. This ease of aircraft operation has resulted in significant growth in the VTOL market. However, stabilized control means that the operator must be able to see the aircraft clearly enough to determine the precise orientation of the aircraft in relation to the object(s) being observed. Applications that require repetitive, precise positioning of the aircraft over an area of interest, such as aerial mapping, are difficult to conduct from the ground-​level perspective of an operator. 2.2.1.3  Automated Control Under automated control, the operator has indirect, assisted control of the aircraft’s flight path. This type of control is typically conducted through a graphical software interface that provides an overhead view of the aircraft’s position overlaid on aerial or satellite imagery (see Figure 2.2). The operator can usually plan the mission in advance with the software’s planning tools, which upload commands to the aircraft during flight to alter the flight path. The aircraft’s autopilot determines the control surface and throttle inputs to position the aircraft on the desired flight path in a three-​dimensional (3-​D) space, and the operator observes the behavior of the aircraft to ensure that the mission is conducted as desired. Automated control requires the least amount of direct operator skill for aircraft control; however, the many software interfaces for UASs vary greatly in complexity. Some interfaces are designed to provide only basic functionality, especially if they are custom-​tailored to a specific aircraft and only need high-​level inputs from the operator. Other interfaces require

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FIGURE 2.2 The Mission Planner software is an example of a graphical interface used for automated missions. (Courtesy of Kansas State University Polytechnic.)

operator input for every possible variable in the mission and can take a significant time to learn. Regardless of the interface, automated control can greatly increase the efficiency and reduce the workload required for a particular mission. Repetitive flight paths, such as orbits and mapping missions, are particularly well suited to automated control.

2.3  Payloads Payload is defined as the total weight a drone or UAV can carry. It does not include the weight of the drone, but is anything that can be added to the drone. This includes cameras, sensors, military armament, packages for delivery, or even passengers. Larger payloads over longer distances give more commercial operational capability. One example is light detection and ranging (LiDAR) technology with infrared (IR), RGB, and video cameras on large unmanned cargo aircraft (LUCA) to deliver cargo. However, larger payloads require larger UAVs and have greater power requirements. Varying factors must be considered to decide which payload is needed. The most important variants are the mission goals and customer requirements, which set the parameters for the payload and aircraft type. This allows the operator to calculate the total payload weight and power requirements and choose the optimal equipment for each mission. When a mission requires that multiple types of payloads be combined on one UAV, the available space, mounting hardware, overall peak power requirements, shared data links, stabilization requirements, and other factors must also be taken into account. The most common types of payload currently used on small UAS (sUAS) are electro-​ optical (EO) and IR referred to as EO/​IR. The EO/​IR sensors are operated on gimbals and turrets or can be laid flat with the nadir pointing down.

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2.3.1  Remote Sensing Remote sensing is defined as “the science of gathering data on an object or area from a considerable distance, as with radar or infrared photography, to observe the earth or heavenly body” (Dictionary.com, 2015). This definition is purposefully broad and it encompasses many common UAS applications. While it is not possible to list every remote-​sensing application of UASs in the context of this chapter, it is worthwhile to note a few key examples. The common thread of these examples is that they involve the remote observation of the earth in order to measure some characteristics thereof. These measurements can range from plant health to the topography of a given area. Most remote-​sensing applications require careful control of how the data are collected in order to make precise measurements, thus making remote sensing one of the more challenging applications to conduct accurately. 2.3.2  Passive Electro-​Optical Sensors EO sensor types are passive, meaning that they collect reflected light energy or heat (thermal) energy emitted from objects. EO sensors refer to those sensor types that can collect and process visible and nonvisible electromagnetic (EM) energy. Many texts refer to EO sensors as measuring only the visible EM spectrum but this is not true. Technically, an EO sensor can operate in any part of the ultraviolet (UV), visible, or IR portion of the EM spectrum. It is common industry practice to differentiate the types of sensors with the designations RGB (visible), UV, IR, near-​infrared (NIR), short wave infrared (SWIR), or middle wavelength infrared (MWIR). Table 2.1 details the sensor types and commercial applications for various passive electro optical wavebands. Figure 2.3 shows the electromagnetic spectrum based on wavelength. Table 2.1 shows the infared section of the spectrum broken out in microns from NIR to LWIR. 6,400°F 2,150°F .76 um 2 um Short wave

Gamma Rays 1 pm 10–4 um

X Rays

850°F 4 um

Medium wave

Ultraviolet Rays

1 nm 10–3 um

103 um Long wave

Infrared

1 um

Microwave

1 mm 103 um

In general, discussions on infrared wavelengths are categorized as either short, medium and long. A more precise description of these wavelengths is in Microns, a unit of measure that is th 1000 of a millimeter.

Radio Waves 1m 104 um

1 km 105 um

Visible light

FIGURE 2.3 The electromagnetic spectrum describes the various types of electromagnetic energy based on wavelength (Protherm, n.d.).

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TABLE 2.1 Passive Electro-​Optical Sensor Chart Sensor Acronym

Meaning

Electromagnetic Waveband

Commercial Applications Filming, photography, construction, engineering, inspection, navigation, scientific and medical research, surveillance, search, and rescue. Mapping, surveying, military Scientific and medical research, archeology, geology, artistic photography Agriculture, forestry, infrastructure inspection, construction, engineering, research, military, geology, search and rescue, mapping, surveying

Visible (RGB)

Daytime imaging using charged coupled device detector (CCD) arrays, Provides the friendliest image to the human observer. Dependent on reflected light energy

0.4–​0.75 µm

UV

Measures the intensity of UV radiation.

0.4–​0.001 µm

NIR

Most NIR sensors will see out to 0.75–​1.1 µm 1 µm. Can enable visible sensors to see better at dawn and dusk or poor visibility. Example devices include night vision, image intensifiers, low light level TV, etc. EO sensors operating in this region They operate will produce quality images between 1.1 µm using reflected light. It has some and about 2.5–​3 capability to see temperature µm (author-​/​ emission. This enables SWIR to text-​dependent) penetrate haze and poor visibility conditions much better than MWIR & LWIR Temperature thermal emissivity They operate capture is dominant in these sensor between 3 and5 types; reflected light energy is less µm dominant.

SWIR

MWIR

LWIR

Temperature thermal emissivity

They operate between 8 and 12 µm

Agriculture, forestry, infrastructure inspection, engineering, construction, research, geology, search & rescue, surveillance, military

Agriculture, forestry, infrastructure inspection, engineering, construction, research, geology, search & rescue, surveillance, military Agriculture, forestry, infrastructure inspection, engineering, construction, research, geology, search & rescue, surveillance, military

2.3.2.1   Electro-​Optical Imaging System An EO imaging system is a system of electronics and optics that can remotely sense the optical frequency EM energy, collect the sensed optical EM frequency data, convert the sensed optical signals to electrical signals, amplify and process the signals, use the signals to run algorithms, and display the results for human observers to see and use. 2.3.2.2  Visible RGB Sensors Visible sensors operate in the 0.4–​0.75 µm range of the EM spectrum. They require an external source of illumination such as the sun, moon, stars, or artificial light to capture data, and provide the most user-​friendly image for a human observer. The number of commercially available digital visible light sensors on the market today is large. They are often

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used for surveillance or to take pictures and video of infrastructure or agriculture. Many visible RGB sensors are able to capture both still pictures and full-​motion video (FMV). These pictures can be used to create 2-​D or 3-​D representations with photogrammetry programs like Pix4dMapper or Drone Deploy. 2.3.2.3  Full-​Motion Video Sensors FMV sensors collect continuous imagery that can be played back in real time or after collection. FMV captures the dynamic motion of objects and/​or persons and is used in a variety of applications to understand the scenario that is unfolding within the imager’s field of view. FMV is particularly well suited to active environments such as cinematography and law-​enforcement applications. However, most FMV imagers are of significantly lower resolution than still imagers. Even 1080p “high-​definition” video only contains about 2  million pixels per image. Still, imagery might contain over 30  million pixels in each image, which means that 1080p high-​definition video has only 1/​15th the resolution of high quality still imagery. FMV can be used with visible or IR sensor types. Similar to still imagery, FMV can be tagged with metadata describing the imagery. Certain software can “exploit” the imagery to its fullest extent by using this metadata. Typically, the metadata are used to graphically display the location of the camera, the location of the object that the camera is viewing, or to automatically adjust the imagery to remove distortion or other artifacts. The operator must be aware of the metadata requirements prior to the flight mission to ensure that the desired information is captured for the application. 2.3.2.4  IR/​NIR/​SWIR Sensors NIR operates from 0.075 to 1.1 µm on the EM spectrum. NIR is reflected light that is not visible to humans. It enables night vision devices, image intensifiers, and low light level TV. It is currently used for a variety of purposes including agriculture, forestry, military, and law enforcement. SWIR operates between 1.1 and about 3 µm. This is also reflected light that is not visible to humans. SWIR can penetrate haze and poor visibility conditions much better than MWIR and long wave infrared (LWIR). 2.3.2.5  MWIR/​LWIR Sensors Midwave infrared (MWIR) sensors operate between 3 and 5 µm. LWIR operates between 8 and 12 µm. The MWIR and LWIR cameras can sense both reflected light and heat emitted from objects (thermal energy). UAV light amplification sensors, sometimes called image intensification sensors, typically do not work in the daytime because there is too much light. Light amplification requires only a small amount of ambient light or a manmade light illuminator to work properly, but an external illumination source is required. Some examples of light amplification EO sensors are night vision devices and low light level TV cameras. Thermal imaging devices (Forward Looking Infra-​Red (FLIR) cameras) detect thermal (heat) emissions and do not need an external source of illumination. Thermal sensors primarily operate in the MWIR (3–​5 µm) or the LWIR (8–​12 µm) portions of the EM spectrum.

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2.3.3  Active Sensors Active sensors provide their own energy (EM radiation) to illuminate the object or scene they observe. They send a pulse of energy from the sensor to the object and then use the reflected radiation, or backscatter, from that object to measure distances or generate 3-​D maps. The two most common types of active sensors are synthetic aperture radar (SAR) and LiDAR. LiDAR can be used to map and maintain pipelines and other infrastructure more easily than the traditional method, which requires expensive manned airplanes to fly over the target areas. Pilots and observers with binoculars look for land erosion, land encroachment, water shedding, new construction, spills, or other hazards to critical infrastructure. Unfortunately, these surveys are limited by human capabilities, and can be inaccurate or incomplete. Using UAS and LiDAR mapping techniques can reduce the expense and increase the accuracy of the data used to determine infrastructure hazards. 2.3.3.1  LiDAR YellowScan and Velodyne LiDAR are manufacturing commercial LiDAR sensors for UAS use that are lightweight and are built to withstand extreme environments. One example is the Velodyne HDL-​32E LiDAR sensor pictured in Figure 2.4. It is small, lightweight, ruggedly built and features up to 32 lasers across a 40° vertical field of view. The power consumption is typically 12 W, including the interface box and regulated power supply. The HDL-​32E is rated to operate in temperatures from –​10 to +60 °C. It measures only 5.7″ high × 3.4″ in diameter, weighs less than 2 kg, and was designed to exceed the demands of the most challenging real-​world autonomous navigation, 3D mobile mapping, and other LiDAR applications. The HDL-​32 E generates a point cloud of up to 695,000 points per second with a range of up to 100 m and a typical accuracy of ±2 cm (Velodyne Lidar, n.d.).

FIGURE 2.4 Velodyne Lidar HDL-​32E Lidar Sensor for UAS applications (Velodyne Lidar, n.d.).

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2.3.3.2  Radar and Synthetic Aperture Radar Operationally, SAR has several striking advantages. First, with a small physical antenna that operates at wavelengths suitable for long-​range mapping, SAR can provide azimuth resolutions as fine as a foot. Second, by increasing the length of the array in proportion to the range of the area to be mapped, the resolution can be made independent of range. Third, since the array is formed in the signal processor, the basic SAR technique can conveniently be adapted to a wide variety of operational requirements (Stimson, 1998). For example, it can generate terrain maps during the day or night through smoke, haze, fog, or clouds. However, one of the major disadvantages of this sensor is called shadowing, which occurs when something on the ground blocks the radar signal from producing an echo return. Instead of seeing the ground, a dark area is displayed in the SAR image. A good example is when a mountain is in the way of the transmitted radio wave. Because of the slant range of the radar and the mountain’s position, the backside of the mountain will be a shadow. The size of the shadow depends on the angle of the SAR and the position of the mountain. The lower the angle, the longer the shadow will be.

2.4  UAS Software for Commercial Applications In the past decade, the commercial drone industry has seen dramatic advances in UAS technology and software programs. This includes software developed by National Aeronautics and Space Administration (NASA) and industry leaders for unmanned traffic management (UTM), fleet management, flight planning, airspace safety, autopilots, analytical software, change detection, machine learning, and computer vision programs. This section will go over basic software programs used by commercial operators to operate its drone fleets safely within the National Airspace System (NAS). 2.4.1  UAS Fleet Management Software As organizations expand their UAS portfolios, they require a method to control their fleets, the missions they operate, the people who operate them, and the data they collect. This includes being able to supervise multiple simultaneous UAS missions while ensuring airspace regulatory compliance and safety of all the missions the organization operates. Companies such as Measure Ground Control, Kittyhawk, and Skyward are starting to develop what is being called UAS Fleet Management Software (UFMS). The purpose of this type of software program is to help manage organizational UAV fleets, accessories, operators, safety, checklists, maintenance schedules, missions, telemetry, analytics, and all the data the organization collects; and to store all of this data in one secure location within a cloud environment. This software improves project coordination by allowing teams to oversee live or postoperations with an Internet browser. Remote pilots can create and fly missions, while other users can see telemetry data from the drone missions, including live video feeds from remote pilot’s mobile devices. This allows organizations to be more transparent with their information, track team efficiency, and record data to comply with FAA regulations. Stakeholders can even replay entire missions from takeoff to landing. The real-​ time updating of flight logs allows the organization to track equipment usage, pilot flight time, and pilot training. This allows users to have more proactive versus reactive safety

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and UAS maintenance programs. Users are also able to generate reports showing incidents and historical graphs of previous missions. The amount of data that are being collected and stored is immense. Traditionally, the information was stored on individual computers that were not accessible to every user. This made it difficult to transfer files, maps, or reports to users of the system. Fortunately, the UFMS can generate mission analytic reports that are easy to access, read, and use. These programs are now using machine learning and change detection to enhance the safety of the missions and make it easier for human users to identify areas of interest. One other significant advantage of using UFMS is that stakeholders can grant user permissions based on their role in the company. Administrators oversee the entire operation and the fleet by assigning roles for each user; pilots, pilot training, pilot certifications, data analysts, data administrators, asset managers, resource managers, and captains. Users can schedule flight missions and assign drone assets to individual teams or remote pilots. This allows companies to manage numerous flight teams, resources, maintenance processes, and share sensor data with all the stakeholders of the company in locations throughout the world. This enables more people to access the data as they need it to make informed decisions about the missions they operate. 2.4.2  Autopilot Software One key technology enabler is the ability of UFMS to support commercial-​off-​the-​shelf (COTS) drones, as well as custom built ones that use open-​source and COTS autopilot software and hardware. Open-​source autopilot systems such as PX4 and ArduPilot work with the MAVLink communication protocol developed by an open-​source project called Dronecode. This project was started in 2008 by Lorenz Meier, cofounder of Auterion. Dronecode is one open-​source project that is helping supply the drone industry with state-​of-​the-​art software and hardware. PX4 is the autopilot software that operates on the PixHawk hardware platform. The Dronecode project hosted under the Linux Foundation serves as the vendor-​neutral home for PX4, MAVLink, QGroundControl, and Dronecode SDK (DroneCode, n.d.). These systems have become the industry standard over the last decade because they have hundreds of developers contributing to the technology. This enables the technology to come to market faster than it would have otherwise. Consequently, many COTS drones use open-​source autopilot and hardware available for free through the DroneCode project. This allows companies to invest their resources in airframe, propulsion, or sensor system development instead of flight control systems. One such example is the new U.S.-​based drone start-​up manufacturer Impossible Aerospace. They have developed a lightweight all electric quadcopter that can go 45 mph with 1.5 pounds of payload for 1 hour and 18 minutes using PX4 ecosystem and MAVLink communication protocols. This drone manufacturer in Silicon Valley, California, is on a mission to assemble the highest performance electric aircraft, the Impossible US-​1 (Cozzens, 2019). The performance and capability of the Impossible US-​1 pictured in Figure  2.5 is a big improvement over DJI, which has dominated sUAS market for the last few years. 2.4.3  Sensor Data Asset Management Although sensor data are extremely useful for a multitude of applications, users continue to struggle with how to handle the enormous quantity of data that needs to be stored, processed, and transformed into usable products such as digital terrain models (DTMs), digital surface models (DSMs), and 3-​D point cloud models. The basic customer

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FIGURE 2.5 Impossible Aerospace US-​A with FLIR sensor (Cozzens, T., 2019).

requirement is for users to be able to access the data that are needed, when and where it is needed, and to be sure that the data integrity is maintained or guaranteed in the process. The two main processes for sharing sensor data are through the web or through a file system. The Geospatial Intelligence Standards (GIS) group has helped to establish important standards that are used to record imagery. Each image collected contains unique (GIS) metadata about that image that may include time of collection, platform, Geo-​location, speed, altitude, roll, pitch, yaw, and other important data specific to where and when the picture was taken. This information helps provide context and meaning to the image and is sometimes called ancillary data. The ancillary data are extremely useful for managing, analyzing, and distributing the data. 2.4.4  Analytical Photogrammetry Software Photogrammetry software is expanding users’ ability to create highly precise two-​ dimensional (2-​D) and 3-​D maps. In large areas, the drone is flown in a grid pattern with its camera facing straight down (nadir) taking a series of overlapping photographs every 100–​400 feet. Since the photographs have the unique GIS data embedded in each picture, they can be stitched together so that the pixel elements in each picture are tied to the identical pixel elements in other photos. Depending on the map size, identifiable features on the ground, and the amount of picture overlap, there may be 100 or more pictures that can have the same pixel element. To map vertical terrain or building facades, remote pilots fly a multicopter UAS in a grid pattern with the camera in an oblique position. See Figure 2.7 façade scanning software tool from UgCS for a visual illustration of what a vertical flight plan for a building façade might look like. 2.4.5  Change Detection and Machine Learning UAS applications that have incorporated change detection and machine learning have exploded over the last few years. AI and machine learning are changing the fundamental way companies are looking at their operations. Companies like PrecisionHawk

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are capitalizing on this by pivoting their businesses away from just manufacturing drones to providing enterprises with on-​demand aerial intelligence. They collect information in five main areas:  construction, insurance, energy, government, and agriculture markets (PrecisionHawk, 2017). PrecisionHawk is taking the data collected and merging it with data science, 2-​D and 3-​D modeling, deep learning, computer vision, and automation to provide its customers with actionable intelligence. For example, PrecisionHawk partnered with Florida Power and Light (FPL) to help inspect its power lines. They use UAS to fly precisely programmed flight plans along its power line infrastructure, which makes the data they collect more consistent in terms of height and flight path than is possible from a helicopter (Skylogic Research, 2019). One recent problem that has come up in recent years is how to handle the volume of pictures, data points, and video collected. FPL Project Manager Eric Schwartz said at AUVSI Xponential keynote address, “This data represents thousands of miles of inspections and millions of pictures just sitting on multiple share points across multiple locations of its company. This data is the true gold that has yet to be mined.” The value of using drones with machine learning, image recognition, and Artificial Intelligence (AI) is that the enhanced system can help notify the human user that something has changed in a specific area over a certain time period. This saves companies the cost of hiring trained image experts to review all the images and videos for defects and, since human errors are generally greater than AI errors, it increases accuracy. Image recognition software can help identify and categorize machines, parts, and infrastructure –​another way to conserve resources. 2.4.6  Computer Vision For UAS to be fully autonomous, they need to be able to adjust their flight plans so that they do not hit anything. Computer vision, a relatively new term for the commercial UAV industry, allows the UAV to use its exteroceptive sensors to detect and avoid objects in much the same way as humans do with their eyes and brain. Exterceptive sensors include high-​definition visible RGB depth cameras, NIR, FLIR, LiDAR, and SAR cameras. One example of an RGBD camera is the Intel RealSense Tracking Camera T265 (Figure 2.6).

FIGURE 2.6 Intel RealSense Tracking Camera T265.

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It is a stand-​alone simultaneous localization and mapping device for robots, drones, and more. It can operate safely in the most difficult and dangerous locations, including poorly mapped areas, indoors, or in dynamically moving environments. Examples include urban environments, construction sites, warehouses, factories, forests, remote locations, places with other UAS operating in close proximity, or GPS-​denied areas. Because of the computational requirements of running path-​planning algorithms to avoid objects, a companion computer like raspberry pi is needed to process the UAV onboard sensor data. Some stakeholders use MAVROS to run the algorithms and the MAVLink communication protocol to send command output signals to the flight controller. Computer vision integrates 3-​D data from the sensors to help the UAV map dynamic environments in real time and determine distances, speeds, and vectors of all moving objects. This allows the UAV to avoid obstacles and navigate around them to reach the mission destination. 2.4.6.1  Autonomous Flight Path Algorithms Two popular path-​planning algorithms used for UAV computer vision are the Enhanced VFH+ and VFH* algorithms, which have been proven to track and avoid objects while navigating in complex environments. The VFH* is an improvement to the VFH+ and uses the A* search algorithm to minimize the cost and heuristic functions. This is especially advantageous for fast-​moving UAVs in areas where other manned aircraft or UAVs may be operating. The VFH* planning algorithm was enhanced by Johann Borenstein and Iwan Ulrich in 2000 to help calculate trajectory and flight path of robotic vehicles. It is a suitable algorithm for UAVs because it takes into account the kinematic limitations (turning radius of the UV) and also develops real-​time polar histograms of obstacles within the sensor’s field of view. Siegwart, Nourbakhsh, and Scaramuzza (2011) describe using the polar histogram to help calculate the flight path of UAVs. A polar histogram shows the probability that an obstacle is in the UAV flight path. All flight paths identified by the sensors as large enough for the UAV to pass through are listed as possible paths. After this is done, a heuristic cost function is applied to every possible flight paths. The cost function G has three heuristic terms. G = target direction (a) + the aircraft orientation (b) + the previous direction (c). The aircraft orientation (b) = the difference between the new direction and the current direction. The previous direction (c) = the difference between the previously selected direction and the new direction. The calculations are structured so that a large deviation from the goal direction leads to a high cost for the “target direction.” The parameters a, b, and c in the cost function G guide the UAV’s behavior. A strong bias is expressed for values that keep the aircraft closest to the target direction (a). The passage with the least cost that is closest the direction of flight and clear of all objects is the one that is chosen (Siegwart et al., 2011). The quality of current sensors coupled with increased computing power has enabled these algorithms to start detecting even the smallest of objects, including birds, branches on tree, and wires in an urban environment. If GPS is not available during operation of a UAV in a complex urban environment, indoors, or under a thick forest canopy, a method is needed to keep the UAV safely on mission and in control. Multiple exteroceptive sensors with onboard flight planning algorithms like VFH* Computer Vision are not dependent on GPS to navigate autonomously; they are using the information collected from the sensors to develop their own map to navigate safely to the destination. This is also true when landing a UAV without access to global positioning data. The computer vision onboard the UAV is able to land safely or continue its mission safely as needed.

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2.5  Commercial Applications The remainder of the chapter is devoted to a discussion of the use of unmanned aircraft as sensor platforms to acquire data. Subsections will discuss the concept of remote sensing, metric and mapping applications, and imaging for a variety of applications that include inspection of structures and infrastructures, news gathering, cinematography, commercial promotion, law enforcement, emergency management, cargo delivery, search and rescue, commercial agriculture, and reconnaissance among others. Nonvisual applications will also be covered. The section will conclude with a discussion of relevant factors critical to the successful acquisition of remotely sensed data. 2.5.1  Building and Roof Inspections The construction, engineering, real estate, and insurance communities have a strong interest in using UAS for building and roof inspections. Building facade inspection programs are mandated by many cities in the United States to proactively identify unsafe conditions that may present a risk of injury or damage to property. While the specific requirements of each city’s ordinances differ, all regulations stipulate that inspections be performed at regular intervals. For example, in Chicago buildings, 80 feet tall or more must be inspected every 4–​12  years, based on the assigned category of the building (Vertical Access, n.d.). These types of visual inspections are costly and dangerous when performed by highly trained inspectors on a rope or scaffolding. However, stakeholders can now complete these inspections much more safely with UAVs without the need for a waiver from the Federal Aviation Administration (FAA). Since the FAA nationwide deployment of Low Altitude Authorization and Notification Capability (LAANC) and 14 CFR Part 107 went into effect, stakeholders can now obtain approval to operate inspection missions in urban environments even with multiple airports or helipads nearby. However, performing this type of job by flying manually would be extremely difficult even for the most experienced remote pilot, so stakeholders are using autonomous operations to perform these flights safely and accurately. Many use advanced mission planning software programs like UgCS to plan and fly complex missions where hazards or no fly zones might exist. Users create a flight plan vertically across each facade of the building, setting the distances from the building and the overlap. The UgCS automatically calculates the optimal flight path for the vertical grid pattern using either an RGB or thermal camera. The camera is placed at an oblique

FIGURE 2.7 UgCS Façade scanning software tool (UgCS, n.d.).

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angle to the vertical or horizontal axis of the building. Each picture is georeferenced so that users can create 2-​D or 3-​D orthomosaic maps of the building in much the same way as for surveying and mapping missions. Postprocessing of the data can include object identification software to count and identify objects on the building, and change detection algorithms to identify what has changed over time. The drones that would be best suited for this type of inspection would be the Yuneec H520, DJI Mavic 2 Enterprise, DJI M200, DJI Inspire 1 or 2, DJI Matrice 600, Lockheed Martin Indago UAS, or a Mavlink compatible multicopter. An advantage of the DJI Mavic 2 is the spotlight that can light up shadowed architectural recesses for a more thorough inspection. 2.5.2  Aircraft Inspections Drones are being used today to inspect large aircraft for damage, and to deliver parts to line mechanics repairing aircraft on the flight line. Situation where risk to humans can be mitigated, time saved, and resources conserved is where drone operations are most prevalent. For example, FedEx is using drones at their Memphis, Tennessee, hub to detect icing on the cowling and empennage of the MD-​10 and MD-​11 number two engines. These engines are located at the base of the vertical stabilizer, where line mechanics would have to use bucket trucks to inspect them in person. Using drones in this manner has many benefits to FedEx, including reduced risk for people and equipment, reduced inspection time, and reduced line maintenance expenses. Another example of drones being used for aircraft inspection is at ST Engineering Aerospace, a Part 145 repair station performing heavy maintenance checks in Singapore on Air New Zealand Boeing 777s. The inspection drones take a series of high-​definition pictures on a planned route around the aircraft’s exterior. The photos are processed by software called DroScan that can detect and classify defects. “We’ve trialed using DroScan on a number of our aircraft undergoing maintenance inspections in Singapore now and believe using a drone will also help improve inspection quality. In future, there may be an opportunity to use the device in New Zealand, for example to conduct ad hoc inspections after lightning strikes,” said Air NZ chief ground operations officer Carrie Hurihanganui (Chua, 2019). 2.5.3  Oil, Gas, Power Lines, and Nuclear Power Plants The oil and gas industry has been using manned aircraft (fixed wing and helicopters) to inspect oil and gas pipelines for decades. The frequency of the inspections depends on the age of the pipelines, location, and contents. Some require daily monitoring, while others are monitored once every couple of months (Whittaker, 2014). Traditionally manned aircraft fly close to the ground, between 300 and 1,000 feet, with a crew of two to three people (spotters) who perform a nondestructive visual inspection of the pipeline infrastructure. They use airborne sensors, write down what they see from the air, and report deficiencies to the oil and gas companies. Trained staff analyze the data and then disseminate it to other company stakeholders. This manual method of inspection is expensive, dangerous, and ineffective. It is estimated that the industry spends about $50 billion annually to monitor their infrastructure (Whittaker, 2014). Consequently, companies like Marathon Pipeline LLC (MPL) are increasingly using drones for regular pipeline maintenance inspections. The company started using drones to inspect pipeline areas where high water was feared, and have now moved to more advanced inspection methods like mapping and

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emergency response. They use drones to inspect areas that are difficult or hazardous to access on foot, for project and pre-​bid planning, and for emergency response to areas affected by weather or flooding. MPL integrates video and images with its GIS software to create 2-​D and 3-​D maps that provide immediate information, plus an historical look at its flights, pictures, and video. The latest addition to the MPL fleet is a SwellPro SplashDrone, a waterproof drone that can be used in wet weather for quick data access during floods (Kezdi, 2018). Oil and gas companies are starting to use drones to locate traces of hydrocarbon or methane emission, track land owner property encroachment (right of way monitoring), search out damage to the pipeline or infrastructure that is above ground, find hazardous spills, identify unauthorized machinery parked or operated near pipeline areas, and look for water shedding and land erosion issues. Areas where the pipeline crosses above or below a river or culvert require special attention; land erosion in these areas is more likely and may be severe. The GIS software used for these tasks makes stakeholder access to the data easy and fast. The data are stored on the World Wide Web in one location in a standardized format. Stakeholders can log-​in from anywhere to access all the LiDAR data, SAR data, video and image data that have been collected, and the 2-​D and 3-​D maps that were produced from the data analysis. This system also makes it much easier to export reports and files. 2.5.4  Industrial Inspection Industrial inspection is a relatively new application for unmanned systems and is becoming one of the more frequent uses for small UASs. There are numerous applications in different industries, but all share the common goal of inspecting equipment, infrastructure, or hardware for defect identification. Items that might be inspected range from highway bridges to flare stacks and each application has its own set of challenges. 2.5.5  Civil Infrastructure Civil infrastructure is composed of the fundamental facilities and structures that enable our everyday lives, including roads, bridges, tunnels, sewers, and the like. This infrastructure wears out over time from vehicle traffic, weather, and other forces; eventually structures degrade and become unsafe or unusable. Routine inspection of civil infrastructure is crucial for defect identification before the loss of infrastructure integrity. The techniques for infrastructure inspection vary greatly, depending on the type of structure being inspected. UASs can remotely sense defects, but have little ability to interact with the object being inspected. This limits UAS to visual, IR, or other imagers that can sense defects, which are often cracks or deformation of structural components. A  few examples of civil infrastructure inspection include: • Bridge inspection: Small VTOL UASs can fly underneath and beside bridge structures to look for cracks in structural members, excessive weathering, loose hardware, or other defects. Most bridge inspections use FMV or a combination of FMV and still imagery. UASs are much safer than in-​person bridge inspections, significantly reducing the risks generated when a person is hoisted and suspended high above the ground during an inspection. Additionally, UASs can often inspect more quickly and cost-​effectively than other methods currently available.

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• Road condition monitoring: UASs are effective for identifying deteriorating road conditions on both paved and unpaved surfaces (Zhang, 2011). Paved road deterioration in the form of cracks, cupping, or potholes leads to further surface failures and hazards for drivers. Unpaved roads also deteriorate and become hazardous, often more dynamically than paved roads. Fixed-​wing and VTOL aircraft can be used to map road condition with visual or LiDAR sensors. Typically, VTOL aircraft are used in applications where a short stretch of road needs an extremely detailed inspection and fixed wings are used for longer road lengths that do not require as much resolution. One challenge with road inspection is the potential safety hazard of flying a UAS over a road that is concurrently being travelled by motorists. U.S. aviation regulations require that no traffic be on the roads during an inspection, which necessitates the shutdown of the road. This limits some of the utility of a UAS for this application. • Levee and dam inspection: As the levee and dam system ages across much of the globe, there is an increasing need to monitor the deterioration of these structures. UASs are particularly effective at mapping levees for erosion (USACE, 2015) and inspecting dam faces for cracking. Levee erosion detection requires significant terrain modeling precision that is difficult to obtain from a manned aircraft that flies at higher altitudes than most UASs. Additionally, a VTOL aircraft flying close to the face of the dam to inspect it for cracks is much less risky than a human rappelling down the dam face for the same purpose. 2.5.6  Electric Power Industry The electric power industry provides a valuable resource to homes and businesses, but is subject to a host of natural and man-​made adverse conditions. Inspecting and monitoring electric power infrastructure is also inherently dangerous. The combination of adversity and danger has caused the electric power industry to be the focus of many early start-​ups in the commercial UAS sector that have attempted to reduce the cost and risk associated with maintaining this infrastructure. There are many applications in the electric power industry, including, but not limited to: • Detailed structure inspection: Small UASs are capable of delivering extremely high-​ quality, close-​up imagery of electric power structures hardware components such as transmission poles, transformers, and insulators. Loose hardware, damaged insulators, and reduced structural integrity can all be detected by a small UAS flying with an FMV or still imagery payload. A UAS has the unique ability to get a “top down” view of energized transmission components without the hazards associated with a lineman climbing the pole or using a bucket truck. This means that a detailed UAS inspection can be completed faster and with less human risk than traditional inspection methods. • Long distance transmission line inspection: Many UASs, including small gas-​powered aircraft, are capable of extraordinarily long endurance. This long endurance makes the UAS ideal for flying long distances over transmission lines to quickly reveal any major damage to structures or lines. Some UASs can fly for 8+ hours and inspect hundreds of miles of line in a single flight. However, the current U.S.  regulatory environment prohibits most Beyond Visual Line-​of-​Sight (BVLOS) operations, which prevents this application from being fully exploited. Also, receiving streaming video

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or imagery from a low-​flying aircraft that is many miles from the ground control station may be a challenge because of terrain obstruction of the radio link. • Right-​ of-​ way encroachment and management: Electric power companies must continuously monitor the right-​ of-​ way (ROW) around their transmission lines for encroachment of vegetation or man-​made structures. Falling objects in the ROW of a transmission line have the potential to cause damage if they are near or on the lines or poles. Private landowners may build structures on an ROW that can hinder access for the electric power company maintaining the lines. The UAS can rapidly fly down an ROW and map encroachments to assist with the electric power company’s ROW management. It also provides documentation of ROW encroachments that may become contentious issues with landowners. • Corona inspections: Electrical coronas can waste large amounts of transmission grid energy and have been known to ignite devastating fires. Special cameras that are sensitive to the UV light spectrum are able to detect the “corona discharge” that occurs when there is a significant ionization of the air around an arcing electric power component. A corona camera sees a corona discharge as a flash of light that is often overlaid on visual imagery. Recent developments in sensor technology have reduced the size of corona cameras to the point where they can be mounted on a small UAS and used for routine inspections. 2.5.7  Wind Turbine Inspection As the demand for “clean energy” grows, wind turbines are an increasingly common sight across the continental United States. Wind turbines can stretch as high as 400 feet above the ground, which poses a significant challenge to the maintenance of these complex mechanical devices. Wind turbines are exposed to harsh environmental forces, lightning strikes, airborne particles, and bird strikes that erode or damage the turbine blades over time. Routine inspections of the blades, hub, and tower of wind turbines are necessary to keep them in continual operation. Small UASs have proven to be very effective at detecting blade erosion and damage with both FMV and still imagery. Instead of having a human inspector rappel from the top of a wind turbine tower, a UAS operator can stand on the ground and fly a VTOL aircraft along the stationary turbine blades to look for defects. Often, a damaged blade will “whistle” as it travels through the air; a strong indicator that the turbine needs immediate inspection. Unfortunately, there is currently no proven technology that can remotely identify subsurface blade defects like delamination. This type of inspection still requires physical contact from a human inspector. 2.5.8  Tower/​Antenna Inspection In much the same manner as electric power and wind turbine inspections, radio, cell phone, and other types of towers can be rapidly inspected by a UAS for damage or loose hardware (see Figure 2.6). Any time that a human has to climb to great heights, there is a risk of falling, even with appropriate protective equipment. For many of these inspections, VTOL UASs are able to reduce risk and increase efficiency by keeping inspectors on the ground. However, high-​power transmitters can cause a communication link failure between the aircraft and ground control station. The UAS operator must be aware of the potential effects from transmitters prior to inspecting a tower and take appropriate measures to ensure that control of the aircraft can be maintained (see Figure 2.8).

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FIGURE 2.8 Radio towers are one example of infrastructure benefiting from UAS inspection methods. (Courtesy of Kansas State University Polytechnic.)

2.5.9  Oil and Gas Inspection The oil and gas industry is one of the largest industries in the world, with over 30 billion barrels of oil consumed globally each year (CIA, 2013). The infrastructure required to support this industry is massive, and since uncontrolled oil leakage can cause tremendous environmental damage, it is critical that this infrastructure is properly maintained. UASs are used for multiple applications in the oil and gas industry, including: • Pipeline patrol and inspection: Similar to electric power transmission line inspection, pipelines can be patrolled by a UAS to identify leaks or damage to the pipeline. Pipeline inspection is still primarily conducted using low-​flying manned aircraft, but there is increasing pressure to perform these patrols with unmanned aircraft. A low-​ flying UAS can detect leaks or damage by looking for the effects of a leak on the surrounding vegetation. The visible browning of nearby vegetation will often be the first indication of a leak in a buried gas pipeline. Oil leaking from an above-​ground pipeline will have similar detrimental effects on vegetation. Therefore, inspection of pipelines is often conducted using either FMV or still imagery that is streamed back to an operator or processed into an aerial map. • Flare stack inspection: Flare stacks are used to burn off the excess gas that accumulates during oil extraction or refinement. These stacks are often mounted on tall towers to mitigate the risk of an open flame burning close to the ground. Historically, inspection of flare stacks for deterioration and damage has required that the stack and the extraction or refinement process be shut down while a human climbs up to visually inspect the hardware. Unmanned aircraft can inspect flare stacks without shutting them down and without risking human life to accomplish the inspection. The

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operation of a UAS as a sensor platform significantly increases efficiency and safety of this operation. • Oil and gas exploration: The location of petroleum deposits across the globe is an ongoing effort that requires many types of data. UASs give geologists and geophysicists a new tool to detect the surface characteristics that indicate the presence of subsurface oil and gas deposits. UASs use aerial mapping and surveying techniques to identify these characteristics and indicate areas where ground-​based crews should perform further analysis. They can also remotely collect data from ground-​based seismic sensors when seismic tests are used to map the subsurface geology of an area. 2.5.10  Photogrammetric Applications Photogrammetry is defined as “the science of making reliable measurements by the use of photographs and especially aerial photographs” (Merriam-​Webster.com, 2015). The science of photogrammetry has existed for decades with many early methods being developed during World War II. Photogrammetry allows 3-​D measurements to be made from 2-​D images using the same technique that allows human eyes to see in 3-​D. By collecting overlapping images from different perspectives, the shape of an object can be mathematically determined with great accuracy. Photogrammetric principles lie at the core of numerous UAS applications, three of which are discussed here: aerial mapping, aerial surveying, and volumetric calculations. 2.5.11  Aerial Mapping Aerial mapping is the process of building a map from aerial imagery. An aerial photograph is inherently deformed by camera lens distortion, angle of view, and the topography of the imaged area, and cannot be used as a map without correction. The aerial map is corrected to account for deformation through a process known as orthorectification, which then allows the map to be used to measure distances and scales. This is important for UAS applications for aerial surveying, precision agriculture, and natural resource management, which use aerial mapping as the baseline process. These applications often start with a basic aerial map generated from any number of different sensors, and then analyze it to interpret what the map data means for the particular application (see Figure 2.9). The process of creating an aerial map with unmanned aircraft begins with the collection of aerial imagery of the geographic area of interest. Simply joining consecutive images together can form a basic 2-​D “mosaic” of the area, but the distortion in the images from the camera and the angle of view prevents this mosaic from being accurate. To create an accurate aerial map, a photogrammetric process is used to correct the deformation in individual images and to understand the 3-​D shape of the terrain. The photogrammetric process commonly used for UAS aerial mapping is based on “Structure from Motion” principles that use multiple perspectives of a single object to determine the object’s shape. When applied to aerial mapping, this means that the imagery must be collected so that each image overlaps another one in the forward direction and consecutive strips of images overlap other strips, typically by at least 2/​3rds (or 66%). This allows multiple perspectives of each single point to be captured. After the aerial imagery is collected, advanced software performs the structure from motion calculations to generate a 3-​D model of the targeted area. The general process used by this software is:

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FIGURE 2.9 A 3-​D model of a corn field as generated from a point cloud. (Courtesy of Kansas State University Polytechnic.)

• Identify key points (or features) in each image. • Match key points from each image with similar key points from other images. • Develop a “cloud” of key points that were found in multiple images (the “point cloud”). • Scale the point cloud using ground control points or camera GPS locations. • Increase point cloud density by finding additional key points after the scale and model shape are generally known. • Connect the points in the cloud to create a solid surface, or “mesh.” • Overlay the image texture onto the mesh to create a solid, textured 3-​D model. • The resulting 3-​D model can be exported in many formats, both 3-​D and 2-​D. The model that results from the structure from motion process is often exported to other software suites for further processing or analysis depending upon the type of application. For example, a precision agriculture application that utilizes NIR or multispectral imagery might use additional software to calculate a vegetation index assessing crop health. Regardless of the application, aerial mapping relies on proper collection of aerial imagery. Poorly collected imagery, blurry images, or inadequate image overlap will result in low-​quality maps that may contain significant inaccuracies or holes in the data. 2.5.12  Aerial Surveying Aerial surveying is often confused with aerial mapping, and sometimes, the terms are used interchangeably. However, the term “surveying” is differentiated from mapping by the reference to measurement of physical characteristics. ESRI (2015) defines surveying as “measuring physical or geometric characteristics of the earth. Surveys are often classified by the type of data studied or by the instruments or methods used. Examples include

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geodetic, geologic, topographic, hydrographic, land, geophysical, soil, mine, and engineering surveys.” Many aerial surveys begin with an aerial map of an area of interest, but the process of surveying implies that characteristics are being measured beyond simple aerial imagery. Topographical maps are a good example of an aerial map that is actually a type of survey. The process of creating a topographical (elevation) map from UAS data begins with the collection of imagery as described in Section 2.3.1, which is then processed into a 3-​D model. This 3-​D model can then be exported into Geographic Information System (GIS) software to create contour lines for elevation changes. The contour lines can either be overlaid on top of the imagery or they can form their own map, often with shading to indicate elevation or reliefs. A topographic map allows for rapid identification of the elevation of any point on the map. 2.5.13  Volumetrics Measuring the volume of physical objects or empty spaces is critical to numerous industries, particularly those that rely on knowing the quantity of stockpiled materials or the amount of material removed from an area. The same 3-​D model derived by aerial surveying applications can be used to measure the volume that lies below or above objects in the model. This is especially useful for the mining industry, which must know precisely how much material is removed from a mine to ensure regulatory compliance and assess productivity. A  coal power plant that must maintain a certain number of burn days in stockpiled coal must know exactly how much coal is stored on site. Topographical land surveys have traditionally been used to calculate the amount of stockpiled coal, but a UAS can fly over stockpiles and mines in a relatively short time, build a 3-​D model of the area, and estimate the volume. The process of creating this volume calculation is the same as discussed earlier, although volumetric surveys are particularly sensitive to errors in the 3-​D model. Even small errors can result in significant differences in the calculated volume. 2.5.14  Precision Agriculture A March 2013 report from the Association for Unmanned Vehicle Systems International (AUVSI) stated that agriculture is expected to be the largest market application for UASs by a wide margin (AUVSI, 2013). As the largest industry in the world, agriculture is present in nearly every country and employs millions of people around the globe. As the global population continues to rise, so must global food production. For this reason, new methods for increasing production efficiency and decreasing costs are essential. Many of these operations are turning to a new1 technique known as “precision agriculture.” Precision agriculture is a farm management system that uses information and technology to enhance the production of the farm. Applications of UASs in precision agriculture are numerous and include the following: • Crop health assessment: Every crop is capable of producing a certain yield when it is 100% healthy, so farmers must carefully manage their crop’s health to ensure maximum yields. This includes supplying the crops with adequate nutrients and water and limiting the harmful effects of pests and weeds. However, it is common for natural and man-​made factors to reduce the crops’ health and subsequent yield potential. UASs can assist in the assessment of crop health by remotely sensing the photosynthetic activity of the plants. One way to determine this photosynthetic activity is by

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calculating the vegetation index, which is a relative index of plant “greenness,” or health. There are a variety of vegetation indices that can be used to assess crop health, but the most common index is the Normalized Difference Vegetation Index (NDVI) (see Figure 2.10). The NDVI is calculated by comparing the difference between visible light and NIR light reflected by the plant. Since plants absorb visible and NIR light at different rates based on their photosynthetic activity, NDVI can indicate the relative health of the plant. A farmer or an agronomist can view an NDVI map of a field and rapidly determine which parts of the field are more productive than others. This is very useful information that can inform farm management decisions. • Stand counts: Most agricultural operations sow a desired number of plants per acre to achieve the maximum yield that the nutrients and soil of the particular field can support (i.e., 25,000 plants per acre). However, many factors will affect how many of these plants emerge and grow into healthy crops. Understanding early in the growing season how many plants actually emerged can help the farmer make decisions about whether or not to replant certain areas, and can assist in the development of a reasonable expectation for the field’s yield. A UAS can be flown during the early season, before the plants’ leaves start to overlap each other, to provide accurate stand counts of the crop. This process involves the creation of an aerial map with sufficient resolution to see individual plants and then using a software algorithm to count them. • Crop damage assessments: Natural events, such as hailstorms or droughts, may cause significant loss of large quantities of crops. Many farmers purchase crop insurance that protects them in the event of such a loss. Insurance companies will reimburse the farmer for lost yield based on a comparison of the reduced actual yield on a field that has suffered significant damage, and the typical yield on the same field in a normal year. A UAS can verify the extent of widespread crop damage so that insurance companies can reimburse the farmer for the proper amount of yield loss. This is especially important for claims that are filed as 100% losses.

FIGURE 2.10 An example of an NDVI analysis done on a corn field. (Courtesy of Kansas State University Polytechnic.)

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2.5.15  Natural Resource Management The protection of the world’s natural resources is vital to the sustainability of the planet. In the United States, agencies such as the Bureau of Land Management (BLM), U.S. Geological Survey (USGS), and U.S. Fish and Wildlife Service (USFWS) are tasked with managing the nation’s natural resources. These agencies, among others, must continuously monitor the health of our natural resources and make management decisions that ensure that these resources thrive. For several years, the USGS and BLM have been flying surplus military UASs to assess a variety of natural resources. UAS natural resource applications range from aerial surveys to wildlife monitoring and utilize a host of different technologies. Two examples include: • Impact of the Elwha dam removal: In the summer of 2012, the USGS collaborated with the Bureau of Reclamation and the National Park Service to assess the impact of the Elwha dam removal in the state of Washington. An AeroVironment Raven UAS was flown over the site during and after the removal of the dam. The Department of Interior used the subsequent 3-​ D models to evaluate sediment distribution throughout the river basin and the effects it may have on wildlife and the environment (USGS, 2012). • Census of ground-​nesting pelicans: The USFWS and USGS tested UASs as a means for collecting population data for pelicans nesting in south-​central North Dakota in Summer 2014. Manned aircraft had been used previously for this application, but they often delivered sub-​par image resolution and were flown at potentially hazardous low altitudes. The test showed that small UASs could deliver accurate bird population counts without disturbing the animals and provide the documentation needed to assess population trends over time (USGS, 2014). 2.5.16  Aerial Filming and Photography For the purposes of this chapter, “aerial filming” applications refer to operations that primarily utilize FMV for the sole purpose of providing a moving picture of some scene (see Figure 2.11). Aerial photography refers to still images that are acquired of a scene from an airborne perspective. These terms are used to differentiate applications that may not be as scientifically defined as aerial mapping and remote sensing. 2.5.17  Filmmaking The filmmaking industry captured headlines in the latter part of 2014 as it received the first approved commercial application for small UAS under the FAA’s exemption process in Section 333 of the FAA Modernization and Reform Act of 2012. Filmmakers have long used aerial video from manned helicopters and airplanes to capture unique perspectives for a movie scene. UASs enable aerial shots to be taken from lower altitudes than ever before, even to the extent of flying a camera into or out of a building. They can produce stable, “movie quality” video without the complications of cranes and jibs or the risk of low altitude helicopter flight. Most filmmakers have chosen to fly large multirotor aircraft with professional cameras mounted on stabilized gimbals. These aerial shots are often conducted with the UAS operator flying the aircraft in a stabilized control mode, while a camera operator controls the camera through a live video feed to obtain the shot desired.

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FIGURE 2.11 Aerial views of scenes can now be captured using low-​cost, relatively easy-​to-​use UAS. (Courtesy of Kansas State University Polytechnic.)

2.5.18  Real Estate In an increasingly competitive real estate industry, realtors are constantly looking for new ways to show off properties. The advent of the small UAS has made it possible for the average realtor to obtain high-​quality aerial images of both residential and commercial properties so that prospective buyers can easily see the layout of the property that is for sale. There has been a surge of realtors operating low-​cost small UASs to obtain this aerial perspective in almost every major urban and rural real estate market. The urban real estate industry is an especially challenging environment for UAS operations. The UAS operator must protect the safety of people who may not be aware an aircraft is operating over their heads, and the risk of losing sight of the aircraft behind urban structures is very real. Realtors must be cautious when operating a UAS, especially in urban areas, to ensure that they are following the FAA’s guidance and policies for these operations. 2.5.19  Marketing UASs are now being used to market everything from cars to homes to concerts. The advertising and marketing industry has quickly learned that an aerial perspective can be a strong persuader to consumers of their products. Marketing with a UAS can range from aerial filming to banner towing. In June 2014, a start-​up company flew a 3-​foot × 12-​foot advertising banner from a multirotor aircraft on the Las Vegas strip (Velotta, 2014). It is possible that banner towing that is currently done by manned aircraft may someday be conducted almost entirely by UASs. 2.5.20  News Reporting Major news outlets across the globe are interested in using UASs to provide live video reporting of a wide variety of events as they unfold. News stations in large cities often have

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their own manned helicopters that they use to report on live events, but UASs bring this same capability closer to earth and at a cost that almost every news station could afford. However, by their very nature, most events reported by news stations routinely have many people at the scene. This poses a problem since the FAA has yet to establish protocols for operation of unmanned systems over large groups of people. If even a small UAS were to fall out of the sky into a crowd, there is a substantial risk of injury to those on the ground. Therefore, most of these operations are not currently permitted. 2.5.21  Intelligence, Surveillance, Reconnaissance, and Emergency Response Intelligence, surveillance, and reconnaissance (ISR) missions have historically referred to military operations where an enemy is located and monitored in support of ongoing or future combat missions. However, this same term can be applied to other types of applications in the civil and commercial markets that are not related to the observation of an enemy. Instead, ISR can be used to refer to the collection of information about something or someone. 2.5.22  Law Enforcement The military origin of UASs used to collect intelligence about enemy combatants creates a natural connection to law-​enforcement applications of unmanned systems. The ability of a small UAS to collect real-​time, on-​demand video in a covert manner is viewed by many as the primary application of “drones” in law enforcement. However, there are many other uses beyond covert surveillance that can benefit law enforcement. Two of these applications include: • Accident and crime scene reconstruction: The same photogrammetric process that is used in aerial surveying can be applied to accident and crime scenes to create accurate 3-​ D models of the scene for later analysis. Determining the causal factors behind an automotive accident can be extremely challenging, but the aerial perspective of a UAS image and/​or a 3-​D model of the scene can help investigators identify where the accident first began and who may have been responsible. Additionally, there has been some initial work using 3-​D models derived from UAS data to reconstruct crime scenes and use them to figure out what occurred during the crime (Miller, 2013). • Tactical operations support: Conducting a tactical operation is both stressful and dangerous for law-​enforcement officers (see Figure  2.12). Circumstances like an active shooter in a shopping mall or a hostage taker require police to assess the situation rapidly and precisely. A UAS provides another tool that can be used to enhance situational awareness during these events. They could potentially save lives by identifying dynamics in the scene that cannot be seen or understood from a ground-​based perspective. Tactical operations support applications typically require live-​streaming FMV that is distributed to those on the scene who need to make rapid decisions to support the mission. 2.5.23  Search and Rescue There is perhaps no other application of UAS that has more potential to save human lives than search and rescue. Depending on the situation, minutes can make the difference between saving and losing a life. UASs, particularly small UASs, can be launched rapidly and cover more ground than rescuers on foot. A thermal IR sensor can locate victims

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FIGURE 2.12 Obtaining an aerial perspective provides a strategic advantage. (Courtesy of Kansas State University Polytechnic.)

quickly by sensing their body heat in the ambient surroundings. Manned aircraft can also cover ground rapidly during a search and rescue mission, but it often takes significant time for them to be dispatched from a local airport and fly to the scene. Small UASs can be deployed immediately by those on the scene, saving precious time that may make the difference between rescuing a victim or recovering a body. The Royal Canadian Mounted Police demonstrated a vivid example of the lifesaving capability a UAS provides when they utilized a small UAS in May 2013 to save the life of a driver who was injured in an automotive accident and then stranded in the cold. In this instance, the officers responding to the accident were unable to locate the driver, although they were able to communicate with him via a cell phone. The officers obtained a GPS location from the driver’s phone, but still could not locate him. They ultimately launched a small multirotor aircraft and were able to identify the driver’s heat signature within minutes. They found him unresponsive but alive at the base of a tree (RCMP, 2013). 2.5.24  Signals Intelligence Signals intelligence, often abbreviated as SIGINT by the military, refers to the collection of intelligence through electronic and communications signals. In a military context, SIGINT is used to help determine the location of an enemy and potentially their intentions. In some cases, all that is needed to locate someone of interest is to discover the location of a signal source. In other instances, it may be necessary to intercept the signal in a manner that allows the content of the signal to be interpreted. There is strong potential for SIGINT to be used in civilian applications, primarily for search and rescue purposes. It may not be possible for a lost person to directly contact rescuers, but SIGINT can be used to identify signals radiating from the lost person’s electronic devices to triangulate the individual’s position and locate the person quickly. The use of SIGINT for nonmilitary purposes must

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be conducted with caution to ensure that there is no intrusion on a person’s reasonable right to privacy. 2.5.25  Communications Relay In the aftermath of a major disaster, one of the major challenges faced by first responders is communicating with each other and with potential victims. Cell phone towers are often overwhelmed with call volume or destroyed, and radios cannot reach far enough to ensure consistent communications. As an example, the aftermath of Hurricane Katrina in 2005 left many first responders and families with no way of communicating with victims or each other (AP, 2005). One potential application to ensure that communications can continue in such situations is to use a UAS as a communications relay for cell phones or radio networks. In this application, an unmanned aircraft is equipped with a payload that can serve as a temporary cell phone or radio repeater is positioned in a long-​term orbit over the affected area. The U.S. military has been using UAS as communications relays with significant success for several years, primarily in the Middle East theaters (Carr, 2009). However, operating an unmanned aircraft over a civilian disaster area poses some challenges to air traffic control when there may be numerous rescue aircraft flying in the same airspace. It therefore requires substantial coordination with the governing aviation authority before flight. 2.5.26  Atmospheric Information Collection Atmospheric sampling is one of the few applications for UAS that does not relate directly to imaging or interacting with ground-​based objects. Instead, atmospheric sampling involves the sensing or collection of airborne particulates or gases to identify the characteristics of the atmosphere. Atmospheric sampling has been performed for many years using manned aircraft and balloons, but UASs bring a new capability. They can sample air more effectively and in regions that were previously more challenging to sample, such as extreme low and high altitudes. UASs can also help understand weather patterns and make forecasts by providing information about the temperature, wind speeds, humidity, and other variables at multiple altitudes. 2.5.27  Meteorology In 2010, NASA conducted the first unmanned flights over a tropical cyclone as part of their research on new ways to predict the path and strength of tropical storms (NASA, 2010). This is a prime example of how UASs are being used to further the understanding of weather, including dangerous weather conditions. The information collected from NASA’s Global Hawk UAS provided much higher resolution data than is obtainable from satellite-​based sensors. Other meteorological applications capture data variables such as temperatures, wind speeds, and ozone content to better understand how weather patterns may be changing. The primary benefit of a UAS versus manned aircraft and balloons is its ability to be launched quickly, operate for long durations, and maintain precise control of the sensors’ positioning. A UAS can also fly into weather phenomena, like tornados, which are too dangerous for other aircraft. For example, the University of Colorado Boulder’s Research and Engineering Center for Unmanned Vehicles (RECUV) has performed significant research into the use of UASs to understand tornado development and prediction (Elston, 2011).

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2.5.28  Hazardous Material Detection Another type of atmospheric sampling is the detection of airborne hazardous materials. Detecting toxic substances in the air is critical to the identification of events that may be hazardous to humans or the environment. Gas is most common hazardous substance that may become airborne. Locating these leaks can be challenging because the gas may be odorless and colorless. However, it is still hazardous. Because no humans are onboard, UASs are ideal for this application; the aircraft can be flown into hazardous areas to identify and quantify the substance and potentially locate the source. Gas leaks from chemical plants, petroleum pipelines, or other sources can be found using this method. Early research indicates that multirotor aircraft can enhance currently available sensors when the rotors increase the airflow over the sensors (Gerhardt, Clothier, & Wild, 2014). 2.5.29  Radioactive Material Detection Nuclear radiation can be detected from an unmanned aircraft in much the same way as other hazardous materials. Sensors that can detect radioactive particles are installed on the aircraft, which is then flown through an area where radioactive material is suspected. Information about the quantity and location of the radioactive particles can be gathered to help responders deal appropriately with the material (Pöllänen et al., 2009). Reducing the exposure of humans to potentially harmful levels of radiation is the primary advantage of using an unmanned aircraft for this application. On March 11, 2011, a tsunami caused the meltdown of the Fukushima nuclear power plant in Japan. The area remains too radioactive for humans to enter, even years after the disaster, but robots and unmanned aircraft have been able to successfully assess the damage and levels of radioactivity (Siminski, 2014). It is possible that a UAS could also be used to detect and prevent potential nuclear terrorist activities by locating the radioactive sources before they are detonated. 2.5.30  Applications Requiring Physical Interaction with Substances, Materials, or Objects All of the applications previously discussed in this chapter have involved remotely sensing something, collecting samples of airborne gases and particulates, or intercepting some type of signal. There are several UAS applications in development that would require some type of physical interaction with objects. Physical interaction is very challenging for unmanned aircraft since they can normally operate in an unconstrained 3-​D environment, and physical interaction places limits on how the aircraft may be operated. A few examples of applications that interact with objects are listed here. 2.5.31  Aerial Chemical Application Commonly called “crop dusting,” aerial application of chemicals, primarily for agricultural purposes, has long been conducted using manned aircraft. In largely agrarian regions, such as the U.S. Midwest, a considerable number of aerial applicators fly extremely close to the ground at high speeds to apply fertilizer, pesticides, and herbicides to vast agricultural operations. These flight operations are inherently dangerous due to the proximity of the aircraft to ground objects and their high speeds. Recent developments, especially in Japan, have shown that UASs can be effectively used for aerial application of chemicals. In fact, the Yamaha RMAX sprays much of Japan’s rice crop. One Yamaha business planner

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notes that “in Japan more than 2,500 RMAX helicopters are being used to spray 40 percent of the fields planted to rice—​that country’s number one crop” (UC Davis, 2013). These aircraft are much larger than the current “small UAS” category for which the FAA has promulgated regulations in the United States. Larger UASs will likely require some type of certification under regulations similar to those that currently apply to manned aircraft. Without additional regulations, integration of large UASs into the National Airspace is not likely to be viable in the United States. 2.5.32  Water Sampling Water quality and availability are becoming increasingly major public concerns, as evidenced by recent droughts and significant reductions in aquifer volumes. UASs are already being used to improve water management at multiple universities. Research underway at the University of Nebraska-​Lincoln (UNL) has focused on the collection of water samples from bodies of water to determine water quality or to identify harmful algal blooms (UNL, 2015). UNL created a multirotor UAS system to assist with rapid collection of water samples that can pump water from a lake or stream through a tube to onboard collection bottles. Kansas State University researchers have successfully shown that small UASs can identify and characterize harmful algal blooms (Van der Merwe & Price, 2015), an important precursor to the actual sampling of water with active algal blooms. As this technology progresses out of the university research environment, it is likely that water quality management will be greatly improved by both remote sensing and physical sampling. 2.5.33  Small Unmanned Cargo Aircraft Delivery The small unmanned cargo aircraft (sUCA) delivery industry has expanded over the last few years because of initiatives by Congress, NASA, DOT, FAA, individual states, university research, advisory groups, ASSURE, AUVSI, commercial industry partners like AIRMAP, Amazon, UPS, Wing, and many others not mentioned. Results of this research have developed the tools necessary to safely allow sUCA into the NAS. NASA teams have been researching UTM that will eventually allow sUCA to operate safely in densely packed metropolitan areas. In August 2019, NASA tested these systems in Reno, Nevada, and Corpus Christi, Texas, as part of the UTM TCL4 project. These were the final tests for the UTM project that began in 2015. Results of this research, in the form of airspace integration requirements, have been incrementally transferred to the FAA in 2020 for its implementation of sUCA in the NAS (NASA, 2020). On October 1st, 2019, the UPS drone airline announced that it received full Part 135 certification from the FAA to deliver packages using sUCA beyond visual line of sight. UPS has partnered with Matternet Drone to design and operate a quadcopter sUCA that carries a small, quickly attachable/​detachable brown box to transport medical supplies across medical campuses in the United States. They proved this concept by flying thousands of sUCA flights at the WakeMed medical campus in Raleigh, NC. The current Part 107 remote pilot certification does not allow pilots to operate more than one sUCA at a time, but this unique certification is the first time that the FAA has granted approval to airline pilots to operate multiple sUCAs at a time. This is a revolutionary advancement in sUCA package delivery business in the United States. UPS uses air-​and ground-​based detect and avoid technology to ensure safety, which enables them to go beyond the visual line of sight to deliver medical samples without jeopardy.

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The only other company so far to receive Part  135 drone airline certification is Wing. However, Wing can only operate with one pilot and one sUCA in the United States so far (Garcia, 2019). Wing is a company within Alphabet, Google’s parent company. Alphabet’s Wing spokesperson, Jonathan Bass, says that Wing expects to share its key project findings publicly. In a mature market, Wing estimates delivery by drone could increase sales in metropolitan areas by up to 27.4% per year for participating businesses. There are also environmental benefits. Assuming a high adoption rate across the metropolitan area, Wing estimates that replacing cars with drones for delivery services could reduce carbon dioxide emissions by 40 tons per year (Sondgeroth, 2020). 2.5.34  Large Unmanned Cargo Delivery The U.S. military have been operating LUCA in the NAS for over two decades. Despite this long history of use, commercial use of LUCA in the NAS is relatively new in the commercial aviation industry. Consequently, only a few aircraft types exist today, and only a few new ones are being developed. Through the efforts of the Platform for Unmanned Cargo Aircraft (PUCA), an organization based in the Netherlands, the aviation community is just beginning to understand the commercial possibilities of LUCA. PUCA is trying to generate stakeholders’ interest in LUCA. However, without an existing design, the potential market is unrealized. Without a potential market, the design might never get built. “Everyone is waiting for everyone else; shippers and operators are saying ‘show us the aircraft,’ manufactures are saying ‘tell us if you want it, and we’ll build it’ and governments are saying ‘we can look at if the market asks for it’ (Heerkens, 2017).” PUCA is urging potential operators, shippers, IT companies, and logistics companies to get involved in the project (Collins, 2017). According to the PUCA website, UCA will be an improvement over manned cargo aircraft in many different areas. The duty length of the crew will not be an issue, so cruising speed can be optimized, most likely around 450 km/​h, to consume less fuel. The low cruising speed will make it possible to use small unpaved runways. LUCA do not need a pressurized cabin so that they can be made lighter and simpler. Because the cross section of the fuselage does not need to be circular, as is the case with a pressurized cabin, it can be shaped efficiently to fit, for example, square cargo containers. The cargo area can be relatively small because no humans need to be accommodated. This gives designers the opportunity to use shapes like a Blended Wing Body (BWB) or flying wing as seen in Figure 2.13, which is 15–​20% more aerodynamically efficient than a conventional aircraft shape. The future LUCA concept aircraft was provided by PUCA and developed by the students of the Technical University of Delft Netherlands. As detailed by Collins (2017), the main issue with the integration of LUCA in the NAS is the lack of safety regulations by the FAA. Safety is the FAA’s most important responsibility in the pursuit of integrating LUCA into the NAS. For the public to have a favorable opinion toward LUCA, the safety standards should be equal to or more stringent than those that regulate commercial FAA Part 121 aircraft operators. LUCA operators should be required to have operating certificates to fly in the NAS. LUCA manufacturers should be required to have Type Certificates to sell their LUCA. LUCA remote pilots should be aircraft type rated and instrument rated, and LUCA equipment should be maintained by FAA-​certified airframe and powerplant technicians. Also, it is important that safety management systems (SMS) be required for LUCA operators just as they are with commercial airlines. These proven processes have been shown to decrease accidents, improve safety, and increase

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FIGURE 2.13 LUCAuture LUCA concept by students of the Technical University of Delft (PUCA, 2017).

public confidence in manned airplanes; it is crucial that the integration of LUCA abides by the same regulations.

2.6  Additional Considerations A few brief, though relevant, comments regarding mission planning and data processing and analysis comprise the final section in this chapter. These factors are critical to the successful completion of any mission regardless of the application used. 2.6.1  Mission Planning The methods for conducting the applications described in this chapter vary dramatically in the means used to control the aircraft, the types of payloads used, and the type of information that is collected. Some of the applications, such as wind turbine inspection, must be conducted almost entirely under manual or stabilized control, while others may be fully automated. The payloads may vary from simple off-​the-​shelf consumer cameras to specialized radioactive particulate samplers. Prior to conducting a UAS mission for any application, the following items must be considered: 1

What type of data is to be collected? The type of data that are going to be collected on a UAS mission must be carefully defined before any further plans can be made. This may be as simple as determining whether basic FMV will be adequate to determine the location of a missing person or as complicated as defining the ground sampling distance needed for aerial imagery to determine the difference between a gull and a pelican. Flight missions that are conducted without understanding the data requirements often result in failed completion of the mission’s goals.

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2

3

4

What type of sensor/​payload is needed? Once the type of data is known, the sensor/​payload that will be flown on the aircraft can be selected. There is often a disparity between the data desired and the payload that can be carried by the aircraft, or will fit the mission’s budget parameters. This is the stage where compromises must be made and the viability of the mission determined. What type of aircraft must be used? If possible, the aircraft should not be selected until the payload is known since the data requirements may drive the type of aircraft to either a VTOL or fixed-​wing platform. Selection of the aircraft before the sensor and data parameters are known can result in unusable end products. Will the flight environment enable the desired results to be achieved? The natural, airspace, and regulatory flight environment may hinder the ability of the mission to achieve the desired results. Careful consideration must be given to all of these factors before conducting the operation.

2.6.2  Data Processing and Analysis Many applications require data collected from the UAS to be processed into usable information and analyzed or interpreted. Simple forms of data processing may only locate where the data came from geographically, while complex data processing may be needed to derive accurate products like volumetric surveys. As new applications arise for unmanned systems, the methods for processing and analyzing the data that support the application will continue to be an area of significant development in the UAS industry. In some cases, new methods of data analysis will drive entirely new applications that have not yet been imagined.

2.7  Conclusion The applications presented in this chapter are only a small sample of the multitude of uses for UAS. New applications are developed every day that demonstrate the potential for UAS to affect almost every industry around the globe. With time and use, many applications will become commonplace, while others will prove to be less useful in the long run. The rapid development of advanced aircraft systems, improved sensors, and favorable regulations are likely to increase the value of UAS in everyday life.

Discussion Questions 2.1 Describe the three basic methods of control discussed in this chapter and discuss how the mission, application, and type of data to be acquired may determine which method is used. 2.2 Describe each of the sensors discussed in this chapter and list the various applications for which each would be used.

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2.3

Discuss in detail each of the applications covered in this section. What do you believe is the best platform design and payload package for each one? Support your answer. 2.4 Operations known as 3-​D missions are discussed in this chapter. Give examples of applications that would be considered 3-​D operations. 2.5 Provide examples of applications requiring physical interaction with substances, materials, or objects. 2.6 List and describe in detail those items that must be considered prior to conducting any UAS mission. Mention why you believe each is significant. 2.7 Reflecting on the types of applications discussed, which do you believe would be most likely rely on extensive data processing? Which would likely to be the least data dependent? 2.8 List the EM spectrum wavebands for EO sensors and discuss the commercial applications of each available type. 2 .9 Discuss the pros and cons of using UAS fleet management software for both civil and commercial applications. Describe the software systems available for this purpose and how they are different from each other. 2.10 Describe how to safely plan and operate an UAV mission that will be autonomously operated, beyond visual line of sight, in an urban environment, and where a GPS signal would not be guaranteed.

Note 1 According to Oliver (2010, 4) the term, “precision agriculture,” was first used in 1990, a quarter century ago, “… as the title of a workshop held in Great Falls, Montana.” When viewed in the historical context of the evolution of the art, science, and practice of farming, which has been fundamental to the maintenance of civilization for millennia, the use of precision agriculture is, indeed, a very recent development.

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3 The “System”  in UAS Joshua Brungardt and Kurt Carraway

3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 What Makes Up an Unmanned Aircraft System. . . . . . . . . . . . . . . . . . . . . . . . . 3.2 UAS/​RPA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Fixed Wing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Vertical Takeoff and Landing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Hybrid Platforms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Command and Control Element . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Autopilot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Ground Control Station. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Communication Data Link. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Radio Line-​of-​Sight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Beyond Radio Line-​of-​Sight. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Payload. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Electro-​Optical . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 Thermal Infrared. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.3 Spectral. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.4 Laser. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Launch and Recovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Human Element. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3.1  Introduction 3.1.1  What Makes Up an Unmanned Aircraft System In this chapter, we will briefly discuss the elements that combine to create an unmanned aircraft system (UAS). Most unmanned systems consist of an unmanned aircraft (UA) or remotely piloted aircraft (RPA), human pilot, payload, control elements, and data link communication architecture. Figure  3.1 illustrates a common UAS and how the various elements are combined to create the system.

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Human element Launch and recovery element

Command and control element Unmanned system Unmanned aircraft

Payload

Communication data link

FIGURE 3.1 Elements of the unmanned aircraft system (UAS).

3.2  UAS/​RPA The term “UA” means an aircraft that is operated without the possibility of direct human intervention in or on the aircraft. In more recent years in some sectors, particularly in some branches of the military, there has been a push to change the term UA to RPA or remotely piloted vehicle (RPV). UA is really a misnomer considering how much human involvement is crucial to the operation of the system. UASs are categorized into five groups by the U.S. Department of Defense as seen in Table 3.1. The Federal Aviation Administration (FAA) has traditionally differentiated small UAS (sUAS) (under 55 lbs) from other larger UASs. In the future, the FAA will likely classify UAS into risk-​based classifications depending on their potential impact on public safety. The sUAS will be the first to be integrated into the National Airspace System (NAS), a process that is underway now. 3.2.1  Fixed Wing Fixed-​wing sUAS have many commercial applications. Because of the efficiencies in their design, they generally have better endurance than their vertical takeoff and landing (VTOL) counterparts. Many fixed-​wing sUAS are made up of simple polystyrene foam and/​or 3-​D printed parts. These aircraft often offer relatively high capability and durability and have proven very popular with many users for their ease of deployment. In the military, fixed-​wing UAS conduct many missions, including intelligence gathering, surveillance, and reconnaissance (ISR). Some military fixed-​wing UASs, such as the General Atomics Predator series of aircraft, have been adapted to joint missions that combine ISR and weapons delivery. The Predator™ was originally designed for an ISR mission and had an aircraft designation of RQ-​1. In the military aircraft classification system, the R stands

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TABLE 3.1 U.S. Department of Defense UAS Classification System UAS Category

Max Gross Takeoff Weight

Normal Operating Altitude (feet)

Airspeed

Group 1 Group 2 Group 3 Group 4 Group 5

< 20 pounds 21–​55 pounds < 1,320 pounds > 1,320 pounds