A comprehensive introduction to the emerging research in information-theoretic radar signal processing Signal processing plays a pivotal role in radar systems to estimate, visualize, and leverage useful target information from noisy and distorted radar signals, harnessing their spatial characteristics, temporal features, and Doppler signatures. The burgeoning applications of information theory in radar signal processing provide a distinct perspective for tackling diverse challenges, including optimized waveform design, performance bound analysis, robust filtering, and target enumeration. Information-Theoretic Radar Signal Processing provides a comprehensive introduction to radar signal processing from an information theory perspective. Covering both fundamental principles and advanced techniques, the book facilitates the integration of information theory into radar signal processing, broadening the scope and improving the performance. Tailored to the needs of researchers and students alike, it serves as a valuable resource for comprehending the information-theoretic aspects of radar signal processing. Information-Theoretic Radar Signal Processing readers will also find: Presentation of alternative hypotheses in adaptive radar detection Detailed discussion of topics including resource management and power allocation Direction-of-arrival (DOA) estimation and integrated sensing and communications (ISAC) Information-Theoretic Radar Signal Processing is ideal for graduate students, scientists, researchers, and engineers, who work on the broad scope of radar and sonar applications, including target detection, estimation, imaging, tracking, and classification using radio frequency, ultrasonic, and acoustic methods.

Table of Contents:
About the Editors xvii List of Contributors xix Preface xxiii 1 Information-Theoretic Waveform Design for MIMO Radar Target Detection 1 Bo Tang, Jun Tang, and Petre Stoica 1.1 Introduction 1 1.2 Signal Model and Problem Formulation 4 1.3 Optimal Waveforms for Distributed MIMO Radar in the Absence of Clutter 8 1.4 MM-Based Waveform Design in the Presence of Range-Spread Clutter 11 1.5 Performance Assessment 19 1.6 Conclusion 23 Acknowledgments 24 References 24 2 Multiple Alternative Hypotheses in Adaptive Radar Detection: An Information-Theoretic Approach 29 Pia Addabbo, Danilo Orlando, and Gaetano Giunta 2.1 Introduction 29 2.2 Radar Detection Problems with Multiple Alternative Hypotheses 32 2.3 Detection Architectures and CFAR Properties 37 2.4 Performance Analysis for Application Examples 42 2.5 Conclusions 51 References 53 3 Information-Theoretic Approaches to Radar Target Enumeration 57 Lei Huang and Hing Cheung So 3.1 Introduction 57 3.2 Problem Formulation 59 3.3 LS-MDL Approach 62 3.4 SCD Approaches 71 3.5 Conclusion 82 Acknowledgments 82 References 82 4 Information-Theoretic Compressive Sensing for Time Delay Estimation 87 Yujie Gu, Nathan A. Goodman, and Yimin D. Zhang 4.1 Introduction 87 4.2 Compressive Measurement Model 90 4.3 Compressive Sensing Kernel Optimization 94 4.4 Bayesian Cramér–Rao Bound 99 4.5 Ziv–Zakai Bound 102 4.6 Simulation Results 107 4.7 Conclusions 116 Acknowledgments 117 References 117 5 Entropy-Enhanced One-Bit Compressive Sensing for DOA Estimation 123 Bin Liao, Qianhui You, and Peng Xiao 5.1 Introduction 123 5.2 Signal Model and Problem Formulation 125 5.3 One-Bit CS Algorithms 129 5.4 Entropy-Enhanced One-Bit CS 131 5.5 l1-SEF-Based One-Bit CS 134 5.6 Simulation Results 138 5.7 Conclusions 146 Acknowledgment 147 References 147 6 Information-Theoretic Methods for Waveform Design in Multistatic Radar Imaging 153 Zacharie Idriss, Raghu G. Raj, and Ram M. Narayanan 6.1 Introduction 153 6.2 System Setup 155 6.3 Statistics of Scenes 159 6.4 Mutual Information 163 6.5 Waveform Design Using mi 165 6.6 Application of Bounds 172 6.7 Conclusion 176 References 177 7 Statistical Information Theory in SAR and PolSAR Image Analysis 181 Alejandro C. Frery and Abraão D. C. Nascimento 7.1 Introduction 181 7.2 Statistical Models for SAR and PolSAR Imagery 182 7.3 SIT: Statistical Information Theory 185 7.4 Integrated View of SAR and PolSAR Data Analysis from SIT 189 7.5 Conclusions and Future Work 208 Acknowledgment 210 References 210 8 Information Fusion and Target Tracking: Information-Theoretic Sensor Selection 217 Nianxia Cao, Pramod K. Varshney, Engin Masazade, and Sora Haley 8.1 Introduction 217 8.2 Target Tracking Model 219 8.3 Particle Filtering for Target Tracking 222 8.4 Information-Theoretic Sensor Selection 223 8.5 Sensor Selection Using Multiobjective Optimization 233 8.6 Conclusion 246 References 247 9 Robust Filtering Under Minimum Error Entropy Criterion 251 Siyuan Peng, Lujuan Dang, Badong Chen, and Jose C. Principe 9.1 Introduction 251 9.2 Minimum Error Entropy Criterion 253 9.3 Sparse Adaptive Filter Under Minimum Error Entropy Criterion 255 9.4 Constrained Adaptive Filter Under MEE Criterion 258 9.5 Adaptive Filter Under Quantized Minimum Error Entropy Criterion 263 9.6 Simulation Results 267 9.7 Conclusion 272 Acknowledgments 273 References 273 10 Dynamic Control of Radar Systems Using Information Rate 277 Bryan Paul and Daniel W. Bliss 10.1 Introduction 277 10.2 Signal Model Framework 281 10.3 Information Rate Controlled Radar 286 10.4 Example: Simplified 2D Target Tracking Kalman Filter 290 10.5 Conclusion 311 References 311 11 Power Allocation Strategies for Localization in Distributed Multiple-Radar Architectures 313 Hana Godrich, Athina P. Petropulu, and H. Vincent Poor 11.1 Introduction 313 11.2 Mathematical Modeling 316 11.3 Power Allocation Optimization 320 11.4 Analysis and Discussion 333 11.5 A Broader Discussion on Resource Allocation 340 Appendix 11.A Coefficients for Minimize MLE 341 Appendix 11.B Coefficients for Minimize Power 342 Acknowledgments 342 References 342 12 Information-Theoretic Approach to Fully Adaptive Radar Resource Management 347 Kristine Bell, Chris Kreucher, and Muralidhar Rangaswamy 12.1 Introduction 347 12.2 FARRM System Model 349 12.3 Information-Theoretic Utility Function 354 12.4 Tracking Task 357 12.5 Classification Task 359 12.6 Simulation Example 361 12.7 Conclusion 370 Acknowledgment 370 References 370 13 Information-Theoretic Limits of Integrated Sensing and Communications 375 Yifeng Xiong, Fuwang Dong, and Fan Liu 13.1 Introduction 375 13.2 Capacity-Distortion Theory 377 13.3 Parameter Estimation 385 13.4 Target Detection 393 13.5 Conclusions 401 References 402 14 Ziv–Zakai Bound for Multisource DOA Estimation 405 Zongyu Zhang, Zhiguo Shi, and Arye Nehorai 14.1 Introduction 405 14.2 Preliminaries 408 14.3 ZZB Derivation for Multisource Estimation 411 14.4 Simulation Results 423 14.5 Conclusions and Future Directions 430 Acknowledgment 431 References 431 Index 435