MIMO radar signal processing / edited by Jian Li, Petre Stoica. 6

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Includes bibliographical references and index.

COVER -- TOC$CONTENTS -- PREFACE -- CONTRIBUTORS -- CH$1 MIMO Radar - Diversity Means Superiority -- 1.1 Introduction -- 1.2 Problem Formulation -- 1.3 Parameter Identifiability -- 1.3.1 Preliminary Analysis -- 1.3.2 Sufficient and Necessary Conditions -- 1.3.3 Numerical Examples -- 1.4 Nonparametric Adaptive Techniques for Parameter Estimation -- 1.4.1 Absence of Array Calibration Errors -- 1.4.2 Presence of Array Calibration Errors -- 1.4.3 Numerical Examples -- 1.5 Parametric Techniques for Parameter Estimation -- 1.5.1 ML and BIC -- 1.5.2 Numerical Examples -- 1.6 Transmit Beampattern Designs -- 1.6.1 Beampattern Matching Design -- 1.6.2 Minimum Sidelobe Beampattern Design -- 1.6.3 Phased-Array Beampattern Design -- 1.6.4 Numerical Examples -- 1.6.5 Application to Ultrasound Hyperthermia Treatment of Breast Cancer -- 1.7 Conclusions -- Appendix IA Generalized Likelihood Ratio Test -- Appendix 1B Lemma and Proof -- Acknowledgments -- References -- CH$2 MIMO Radar: Concepts, Performance Enhancements, and Applications -- 2.1 Introduction -- 2.1.1 A Short History of Radar -- 2.1.2 Definition and Characteristics of MIMO Radar -- 2.1.3 Uses of MIMO Radar -- 2.1.4 The Current State of MIMO Radar Research -- 2.1.5 Chapter Outline -- 2.2 Notation -- 2.3 MIMO Radar Virtual Aperture -- 2.3.1 MIMO Channel -- 2.3.2 MIMO Virtual Array: Resolution and Sidelobes -- 2.4 MIMO Radar in Clutter-Free Environments -- 2.4.1 Limitations of Cramér-Rao Estimation Bounds -- 2.4.2 Signal Model -- 2.4.3 Fisher Information Matrix -- 2.4.4 Waveform Correlation Optimization -- 2.4.5 Examples -- 2.5 Optimality of MIMO Radar for Detection -- 2.5.1 Detection -- 2.5.2 High SNR -- 2.5.3 Weak-Signal Regime -- 2.5.4 Optimal Beamforming without Search -- 2.5.5 Nonfading Targets -- 2.5.6 Some Additional Benefits of MIMO Radar -- 2.6 MIMO Radar with Moving Targets in Clutter: GMTI Radars -- 2.6.1 Signal Model -- 2.6.2 Localization and Adapted SNR -- 2.6.3 Inner Products and Beamwidths -- 2.6.4 SNR Loss -- 2.6.5 SNR Loss and Waveform Optimization -- 2.6.6 Area Search Rates -- 2.6.7 Some Examples -- 2.7 Summary -- Appendix 2A A Localization Principle -- Appendix 2B Bounds on R(N) -- Appendix 2C An Operator Norm Inequality -- Appendix 2D Negligible Terms -- Appendix 2E Bound on Eigenvalues -- Appendix 2F Some Inner Products -- Appendix 2G An Invariant Inner Product -- Appendix 2H Krönecker and Tensor Products -- 2H.1 Lexicographical Ordering -- 2H.2 Tensor and Krönecker Products -- 2H.3 Properties -- Acknowledgments -- References -- CH$3 Generalized MIMO Radar Ambiguity Functions -- 3.1 Introduction -- 3.2 Background -- 3.3 MIMO Signal Model -- 3.4 MIMO Parametric Channel Model -- 3.4.1 Transmit Signal Model -- 3.4.2 Channel and Target Models -- 3.4.3 Received Signal Parametric Model -- 3.5 MIMO Ambiguity Function -- 3.5.1 MIMO Ambiguity Function Composition -- 3.5.2 Cross-Correlation Function under Model Simplifications -- 3.5.3 Autocorrelation Function and Transmit Beampatterns -- 3.6 Results and Examples -- 3.6.1 Orthogonal Signals -- 3.6.2 Coherent Signals -- 3.7 Conclusion -- References -- CH$4 Performance Bounds and Techniques for Target Localization Using MIMO Radars -- 4.1 Introduction --T$1.

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Edited and introduced by leading researchers in the field of MIMO radar research, this book introduces recent developments in the area of MIMO radar to stimulate new concepts, theories, and applications of the topic, and to foster further cross-fertilization of ideas with MIMO communications. Written in an easy-to-follow tutorial style, MIMO Radar Signal Processing serves as an excellent course book for graduate students and a valuable reference for researchers in academia and industry.--BACK COVER.

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