Overview

In-depth exploration of machine learning techniques applied to UAV operations and communications, highlighting areas of potential growth and research gaps Artificial Intelligence for Unmanned Aerial Vehicles provides a comprehensive overview of machine learning (ML) techniques used in unmanned aerial vehicle (UAV) operations, communications, sensing, and computing. It emphasizes key components of UAV activity to which ML can significantly contribute including perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. The book considers the notion of security in the UAV network primarily in terms of its underlying rationale. This book also includes a detailed analysis of UAV behavior with respect to time and explores online machine learning-based solutions for UAV-assisted IoT networks. Additional topics include: Joint cruise control and data collection Resilience in an AI-aided UAV network against multiple attacks, introducing a flexible and adaptive threshold to alleviate malicious conduct Quantification of influencing attributes, quantification of weights affiliated with these attributes, and movement tracking of malicious UAVs Integration of contextual information, threshold definitions, and time-variant behavior analysis Artificial Intelligence for Unmanned Aerial Vehicles is an essential up-to-date reference on the subject for researchers, professors, graduate and senior undergraduate students, and industry professionals in the field.

Full Product Details

Author:   Shuyan Hu (Fudan University, China) ,  Xin Yuan (University of New South Wales, Sydney, Australia) ,  Kai Li (University of Cambridge, UK) ,  Wei Ni (University of New South Wales, Sydney, Australia)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-IEEE Press
ISBN:  

9781394369454

ISBN 10:   139436945
Pages:   416
Publication Date:   17 February 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Forthcoming
Availability:   Awaiting stock  

Table of Contents

List of Figures xi List of Tables xxi About the Authors xxiii Preface xxvii Acronyms xxix 1 Introduction 1 1.1 UAV Applications 1 1.2 Sensing, Communication, and Computing of UAVs 2 1.3 Mobility Models of UAVs 4 1.4 UAVs Powered with AI 4 1.5 Book Contributions 6 1.6 Book Outline 8 2 Capacity and Connectivity of UAVs in Three-dimensional Spaces 11 2.1 Introduction 11 2.2 System Model and Performance Metrics 13 2.3 Analysis of UAVs with Random Trajectories 20 2.4 Network Densification and Relay 29 2.5 Instantaneous Outage Probability of UAVs Under Ground Interference 31 2.6 Ergodic Outage Probability of UAVs Under Ground Interference 36 2.7 Ergodic Outage Probability of UAVs Without Ground Interference 39 2.8 Numerical Evaluation 40 2.9 Impact of Imperfect CSI at UAV Receiver 44 2.10 Conclusion 52 3 Secrecy Risks Posed by Individual UAV Adversary 53 3.1 Introduction 53 3.2 System Model 56 3.3 Achievable Secrecy Rate Under Aerial Eavesdropper 61 3.4 Numerical and Simulation Results 67 3.5 Conclusion 76 4 Secrecy Risks Posed by Cooperative UAV Adversaries 77 4.1 Ergodic and ;;-Outage Secrecy Capacity Against Cooperative Aerial Eavesdroppers 77 4.2 Secrecy Under Imperfect Oscillator and Finite Memory of Eavesdroppers 85 4.3 Numerical and Simulation Results 89 4.4 Discussion on AI Techniques and Noncooperative Eavesdroppers 95 4.5 Conclusion 96 5 Joint Cruise Control and Data Collection of UAVs 99 5.1 Introduction 99 5.2 Existing Techniques 102 5.3 System Model 104 5.4 Joint Optimization of Cruise Control and Task Offloading 106 5.5 Combinatorial GNN-enabled DRL 108 5.6 Implementation and Performance Evaluation 112 5.7 Conclusion 118 6 Radio and Video Surveillance with UAVs 119 6.1 Introduction 119 6.2 System Model 122 6.3 Trajectory and Power Optimization for Rotary-wing UAV 129 6.4 Trajectory and Power Optimization for Fixed-wing UAV 140 6.5 Numerical Results 144 6.6 Authentication Management of UAVs 168 6.7 Conclusion 169 7 Energy Efficiency for UAV-based Radio Sensing and Monitoring 171 7.1 Introduction 171 7.2 System Models 174 7.3 Legitimate Eavesdropping with Jamming 178 7.4 Energy Management for Solar-powered UAV 186 7.5 Numerical Results 193 7.6 Conclusion 200 8 Joint Sensing and Communications with UAVs 201 8.1 Introduction 201 8.2 System Model 207 8.3 UAV-assisted JCAS 213 8.4 Problem Formulation and DDPG-based Solution for Visual-based UAV 219 8.5 Simulation and Experiment 229 8.6 Conclusion 251 9 Reconfigurable Intelligent Surface-assisted UAVs 253 9.1 Introduction 253 9.2 DRL-driven RIS-assisted Radio Surveillance 256 9.3 RIS-assisted Jamming Rejection for UAV-borne IoT 272 9.4 Performance Evaluation 281 9.5 Conclusion 298 10 Conclusion and Future Work 301 A Appendix of Chapter 2 303 B Appendix of Chapter 3 307 C Appendix of Chapter 4 321 Bibliography 337 Index 367

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Author Information

SHUYAN HU, PHD, is an Associate Professor with the College of Electronics and Information Engineering at Tongji University, Shanghai, China. XIN YUAN, PHD, is a Senior Research Scientist at CSIRO and an Adjunct Senior Lecturer at the University of New South Wales, Sydney, NSW, Australia. KAI LI, PHD, serves as a Visiting Research Scientist with the School of Electrical Engineering and Computer Science, TU Berlin, Germany, and is also a Senior Research Scientist with Real-Time and Embedded Computing Systems Research Centre (CISTER), Porto, Portugal. WEI NI, PHD, is a Senior Principal Research Scientist at CSIRO and a Conjoint Professor at the University of New South Wales, Sydney, NSW, Australia. XIN WANG, PHD, is a Professor with the College of Future Information Technology at Fudan University, Shanghai, China.

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