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
