{"product_id":"information-theoretic-radar-signal-processing-isbn-9781394216925","title":"Information-Theoretic Radar Signal Processing","description":"\u003cp\u003e\u003cb\u003eA comprehensive introduction to the emerging research in information-theoretic radar signal processing\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eSignal 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eInformation-Theoretic Radar Signal Processing\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eInformation-Theoretic Radar Signal Processing\u003c\/i\u003e readers will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ePresentation of alternative hypotheses in adaptive radar detection\u003c\/li\u003e\n\u003cli\u003eDetailed discussion of topics including resource management and power allocation\u003c\/li\u003e\n\u003cli\u003eDirection-of-arrival (DOA) estimation and integrated sensing and communications (ISAC)\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eInformation-Theoretic Radar Signal Processing\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003eAbout the Editors xvii\u003c\/p\u003e \u003cp\u003eList of Contributors xix\u003c\/p\u003e \u003cp\u003ePreface xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Information-Theoretic Waveform Design for MIMO Radar Target Detection 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBo Tang, Jun Tang, and Petre Stoica\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Signal Model and Problem Formulation 4\u003c\/p\u003e \u003cp\u003e1.3 Optimal Waveforms for Distributed MIMO Radar in the Absence of Clutter 8\u003c\/p\u003e \u003cp\u003e1.4 MM-Based Waveform Design in the Presence of Range-Spread Clutter 11\u003c\/p\u003e \u003cp\u003e1.5 Performance Assessment 19\u003c\/p\u003e \u003cp\u003e1.6 Conclusion 23\u003c\/p\u003e \u003cp\u003eAcknowledgments 24\u003c\/p\u003e \u003cp\u003eReferences 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Multiple Alternative Hypotheses in Adaptive Radar Detection: An Information-Theoretic Approach 29\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePia Addabbo, Danilo Orlando, and Gaetano Giunta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 29\u003c\/p\u003e \u003cp\u003e2.2 Radar Detection Problems with Multiple Alternative Hypotheses 32\u003c\/p\u003e \u003cp\u003e2.3 Detection Architectures and CFAR Properties 37\u003c\/p\u003e \u003cp\u003e2.4 Performance Analysis for Application Examples 42\u003c\/p\u003e \u003cp\u003e2.5 Conclusions 51\u003c\/p\u003e \u003cp\u003eReferences 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Information-Theoretic Approaches to Radar Target Enumeration 57\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLei Huang and Hing Cheung So\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 57\u003c\/p\u003e \u003cp\u003e3.2 Problem Formulation 59\u003c\/p\u003e \u003cp\u003e3.3 LS-MDL Approach 62\u003c\/p\u003e \u003cp\u003e3.4 SCD Approaches 71\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 82\u003c\/p\u003e \u003cp\u003eAcknowledgments 82\u003c\/p\u003e \u003cp\u003eReferences 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Information-Theoretic Compressive Sensing for Time Delay Estimation 87\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYujie Gu, Nathan A. Goodman, and Yimin D. Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 87\u003c\/p\u003e \u003cp\u003e4.2 Compressive Measurement Model 90\u003c\/p\u003e \u003cp\u003e4.3 Compressive Sensing Kernel Optimization 94\u003c\/p\u003e \u003cp\u003e4.4 Bayesian Cramér–Rao Bound 99\u003c\/p\u003e \u003cp\u003e4.5 Ziv–Zakai Bound 102\u003c\/p\u003e \u003cp\u003e4.6 Simulation Results 107\u003c\/p\u003e \u003cp\u003e4.7 Conclusions 116\u003c\/p\u003e \u003cp\u003eAcknowledgments 117\u003c\/p\u003e \u003cp\u003eReferences 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Entropy-Enhanced One-Bit Compressive Sensing for DOA Estimation 123\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBin Liao, Qianhui You, and Peng Xiao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 123\u003c\/p\u003e \u003cp\u003e5.2 Signal Model and Problem Formulation 125\u003c\/p\u003e \u003cp\u003e5.3 One-Bit CS Algorithms 129\u003c\/p\u003e \u003cp\u003e5.4 Entropy-Enhanced One-Bit CS 131\u003c\/p\u003e \u003cp\u003e5.5 l\u003csub\u003e1\u003c\/sub\u003e-SEF-Based One-Bit CS 134\u003c\/p\u003e \u003cp\u003e5.6 Simulation Results 138\u003c\/p\u003e \u003cp\u003e5.7 Conclusions 146\u003c\/p\u003e \u003cp\u003eAcknowledgment 147\u003c\/p\u003e \u003cp\u003eReferences 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Information-Theoretic Methods for Waveform Design in Multistatic Radar Imaging 153\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eZacharie Idriss, Raghu G. Raj, and Ram M. Narayanan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 153\u003c\/p\u003e \u003cp\u003e6.2 System Setup 155\u003c\/p\u003e \u003cp\u003e6.3 Statistics of Scenes 159\u003c\/p\u003e \u003cp\u003e6.4 Mutual Information 163\u003c\/p\u003e \u003cp\u003e6.5 Waveform Design Using mi 165\u003c\/p\u003e \u003cp\u003e6.6 Application of Bounds 172\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 176\u003c\/p\u003e \u003cp\u003eReferences 177\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Statistical Information Theory in SAR and PolSAR Image Analysis 181\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAlejandro C. Frery and Abraão D. C. Nascimento\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 181\u003c\/p\u003e \u003cp\u003e7.2 Statistical Models for SAR and PolSAR Imagery 182\u003c\/p\u003e \u003cp\u003e7.3 SIT: Statistical Information Theory 185\u003c\/p\u003e \u003cp\u003e7.4 Integrated View of SAR and PolSAR Data Analysis from SIT 189\u003c\/p\u003e \u003cp\u003e7.5 Conclusions and Future Work 208\u003c\/p\u003e \u003cp\u003eAcknowledgment 210\u003c\/p\u003e \u003cp\u003eReferences 210\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Information Fusion and Target Tracking: Information-Theoretic Sensor Selection 217\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNianxia Cao, Pramod K. Varshney, Engin Masazade, and Sora Haley\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 217\u003c\/p\u003e \u003cp\u003e8.2 Target Tracking Model 219\u003c\/p\u003e \u003cp\u003e8.3 Particle Filtering for Target Tracking 222\u003c\/p\u003e \u003cp\u003e8.4 Information-Theoretic Sensor Selection 223\u003c\/p\u003e \u003cp\u003e8.5 Sensor Selection Using Multiobjective Optimization 233\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 246\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Robust Filtering Under Minimum Error Entropy Criterion 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSiyuan Peng, Lujuan Dang, Badong Chen, and Jose C. Principe\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 251\u003c\/p\u003e \u003cp\u003e9.2 Minimum Error Entropy Criterion 253\u003c\/p\u003e \u003cp\u003e9.3 Sparse Adaptive Filter Under Minimum Error Entropy Criterion 255\u003c\/p\u003e \u003cp\u003e9.4 Constrained Adaptive Filter Under MEE Criterion 258\u003c\/p\u003e \u003cp\u003e9.5 Adaptive Filter Under Quantized Minimum Error Entropy Criterion 263\u003c\/p\u003e \u003cp\u003e9.6 Simulation Results 267\u003c\/p\u003e \u003cp\u003e9.7 Conclusion 272\u003c\/p\u003e \u003cp\u003eAcknowledgments 273\u003c\/p\u003e \u003cp\u003eReferences 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Dynamic Control of Radar Systems Using Information Rate 277\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eBryan Paul and Daniel W. Bliss\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 277\u003c\/p\u003e \u003cp\u003e10.2 Signal Model Framework 281\u003c\/p\u003e \u003cp\u003e10.3 Information Rate Controlled Radar 286\u003c\/p\u003e \u003cp\u003e10.4 Example: Simplified 2D Target Tracking Kalman Filter 290\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 311\u003c\/p\u003e \u003cp\u003eReferences 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Power Allocation Strategies for Localization in Distributed Multiple-Radar Architectures 313\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHana Godrich, Athina P. Petropulu, and H. Vincent Poor\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 313\u003c\/p\u003e \u003cp\u003e11.2 Mathematical Modeling 316\u003c\/p\u003e \u003cp\u003e11.3 Power Allocation Optimization 320\u003c\/p\u003e \u003cp\u003e11.4 Analysis and Discussion 333\u003c\/p\u003e \u003cp\u003e11.5 A Broader Discussion on Resource Allocation 340\u003c\/p\u003e \u003cp\u003eAppendix 11.A Coefficients for Minimize MLE 341\u003c\/p\u003e \u003cp\u003eAppendix 11.B Coefficients for Minimize Power 342\u003c\/p\u003e \u003cp\u003eAcknowledgments 342\u003c\/p\u003e \u003cp\u003eReferences 342\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Information-Theoretic Approach to Fully Adaptive Radar Resource Management 347\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKristine Bell, Chris Kreucher, and Muralidhar Rangaswamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 347\u003c\/p\u003e \u003cp\u003e12.2 FARRM System Model 349\u003c\/p\u003e \u003cp\u003e12.3 Information-Theoretic Utility Function 354\u003c\/p\u003e \u003cp\u003e12.4 Tracking Task 357\u003c\/p\u003e \u003cp\u003e12.5 Classification Task 359\u003c\/p\u003e \u003cp\u003e12.6 Simulation Example 361\u003c\/p\u003e \u003cp\u003e12.7 Conclusion 370\u003c\/p\u003e \u003cp\u003eAcknowledgment 370\u003c\/p\u003e \u003cp\u003eReferences 370\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Information-Theoretic Limits of Integrated Sensing and Communications 375\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eYifeng Xiong, Fuwang Dong, and Fan Liu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 375\u003c\/p\u003e \u003cp\u003e13.2 Capacity-Distortion Theory 377\u003c\/p\u003e \u003cp\u003e13.3 Parameter Estimation 385\u003c\/p\u003e \u003cp\u003e13.4 Target Detection 393\u003c\/p\u003e \u003cp\u003e13.5 Conclusions 401\u003c\/p\u003e \u003cp\u003eReferences 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Ziv–Zakai Bound for Multisource DOA Estimation 405\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eZongyu Zhang, Zhiguo Shi, and Arye Nehorai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 405\u003c\/p\u003e \u003cp\u003e14.2 Preliminaries 408\u003c\/p\u003e \u003cp\u003e14.3 ZZB Derivation for Multisource Estimation 411\u003c\/p\u003e \u003cp\u003e14.4 Simulation Results 423\u003c\/p\u003e \u003cp\u003e14.5 Conclusions and Future Directions 430\u003c\/p\u003e \u003cp\u003eAcknowledgment 431\u003c\/p\u003e \u003cp\u003eReferences 431\u003c\/p\u003e \u003cp\u003eIndex 435\u003c\/p\u003e \u003cp\u003e\u003cb\u003eYujie Gu, PhD,\u003c\/b\u003e currently works as a Senior Radar Scientist at Aptiv Advanced Engineering Center, Agoura Hills, California. He is an Associate Editor of \u003ci\u003eIEEE Transactions on Signal Processing\u003c\/i\u003e, a Subject Editor-in-Chief of \u003ci\u003eElectronics Letters\u003c\/i\u003e, an Editor of \u003ci\u003eSignal Processing\u003c\/i\u003e, and an elected member of the Sensor Array and Multichannel (SAM) Signal Processing Technical Committee and the Signal Processing Theory and Methods (SPTM) Technical Committee of the IEEE Signal Processing Society. He is a Senior Member of IEEE.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eYimin D. Zhang, PhD,\u003c\/b\u003e is currently an Associate Professor with the Department of Electrical and Computer Engineering at Temple University, Philadelphia, Pennsylvania. He is a Senior Area Editor of \u003ci\u003eIEEE Transactions on Signal Processing\u003c\/i\u003e, an Editor of \u003ci\u003eSignal Processing\u003c\/i\u003e, and an elected member of the Signal Processing Theory and Methods (SPTM) Technical Committee of the IEEE Signal Processing Society. He is a Fellow of IEEE, a Fellow of SPIE, and a Distinguished Lecturer of the IEEE Signal Processing Society.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eA comprehensive introduction to the emerging research in information-theoretic radar signal processing\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eSignal 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eInformation-Theoretic Radar Signal Processing\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eInformation-Theoretic Radar Signal Processing\u003c\/i\u003e readers will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ePresentation of alternative hypotheses in adaptive radar detection\u003c\/li\u003e\n\u003cli\u003eDetailed discussion of topics including resource management and power allocation\u003c\/li\u003e\n\u003cli\u003eDirection-of-arrival (DOA) estimation and integrated sensing and communications (ISAC)\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eInformation-Theoretic Radar Signal Processing\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989423210725,"sku":"NP9781394216925","price":150.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394216925.jpg?v=1761784046","url":"https:\/\/k12savings.com\/es\/products\/information-theoretic-radar-signal-processing-isbn-9781394216925","provider":"K12savings","version":"1.0","type":"link"}