Signal Design for Modern Radar Systems

This book provides a comprehensive overview of key optimization tools that can be used to design radar waveforms and adaptive signal processing strategies, assisting you in meeting the increased demands of sensing system proliferation. These methods include power-methodlike iterations, coordinate descent, majorization-minimization, block successive upper-bound minimization, and semidefinite programming. This book walks you through these optimization frameworks that achieve the desired design goal for waveform design as a solution to a constrained optimization problem, such as finite energy, unimodularity (or having constantmodulus), and finite or discrete-phase (potentially binary) alphabet, which are dictated by practical constraints of real systems. Focusing on a holistic approach rather than a problemspecific approach, the book shows what you need to formulate waveform design effectively and to understand the flexibility of the framework for adapting to your own specific needs. By reading this book, you will have full access to the tools and knowledge required to design waveforms with optimized correlation/cross-correlation properties in different dimensions (e.g., time or space) for multiple radar configurations such as SISO/SIMO and MIMO radars, while considering spectral constraints for emerging topics such as cognitive radar, coexistence with communications, and mitigation of potential Doppler and quantization errors. It also includes sample software programs to assist you in generating the described solutions. This is a detailed handbook for industry researchers, scientists, and designers, including medical, marine, defense, and automotive companies, due to its unique style of covering mathematical results as well as their applications from various areas. With many exercise problems, the book is also an excellent resource for advanced courses on radar signal processing. Introduction Practical Signal Design The Why The How The What Radar Application Focus Areas Designing Signals with Good Correlation Properties Signal Design to Enhance SINR Spectral Shaping and Coexistence with Communications Automotive Radar Signal Processing and Sensing for Autonomous Vehicles What this Book Offers References Convex and Nonconvex Optimization Optimization Algorithms Gradient Descent Algorithm Newton's Method Mirror Descent Algorithm Power Method-Like Iterations Majorization-Minimization Framework Block Coordinate Descent Alternating Projection Alternating Direction Method of Multipliers Summary of the Optimization Approaches Conclusion References PMLI The PMLI Formulation Fixed-Energy Signals Unimodular or Constant-Modulus Signals Discrete-Phase Signals PAR-Constrained Signals Convergence of Radar Signal Design PMLI and the Majorization-Minimization Technique: Points of Tangency Application of PMLI A Toy Example: Synthesizing Cross-Ambiguity Functions PMLI Application with Dinkelbach's Fractional Programming Doppler-Robust Radar Code Design Radar Code Design Based on Information-Theoretic Criteria MIMO Radar Transmit Beamforming Matrix PMLI Derivation for (3.71) and (3.75) Conclusion Exercise Problems References MM Methods System Model MM Method MM Method for Minimization Problems MM Method for Minimax Problems Sequence Design Algorithms ISL Minimizers PSL Minimizers Numerical Simulations Conclusion Exercise Problems References BCD Method The BCD Method Rules for Selecting the Index Set Convergence of BCD BSUM: A Connection Between BCD and MM Applications Application 1: ISL Minimization Application 2: PSL Minimization Application 3: Beampattern Matching in MIMO Radars Conclusion Exercise Problems References Appendix 5A Appendix 5B Appendix 5C Other Optimization Methods System Model System Model in the Spatial Domain System Model in the Spectrum Domain Problem Formulation Optimization Approach Convergence Computational Complexity Numerical Results Convergence Analysis Performance Evaluation The Impact of Similarity Parameter The Impact of Zero Padding Conclusion References Appendix 6A Deep Learning for Radar Deep Learning for Guaranteed Radar Processing Deep Architecture for Radar Processing Numerical Studies and Remarks Deep Radar Signal Design The Deep Evolutionary Cognitive Radar Architecture Performance Analysis Conclusion Exercise Problems References Waveform Design in 4-D Imaging MIMO Radars Beampattern Shaping and Orthogonality System Model Problem Formulation Design Procedure Using the CD Framework Solution for Limited Power Constraint Solution for PAR Constraint Solution for Continuous Phase Solution for Discrete Phase Numerical Examples Contradictory Nature of Spatial and Range ISLR Trade-Off Between Spatial and Range ISLR The Impact of Alphabet Size and PAR Conclusion Exercise Problems References Appendix 8A Appendix 8B Appendix 8C Appendix 8D Appendix 8E Waveform Design for Spectrum Sharing Scenario and Signal Model Communication Link and CSI Transmit Signal Model Signal Model at Targets Backscatter Signal Model Clutter Model Signal Model at ACV CSI Exploitation Performance Indicators ACV SNR Evaluation SCNR at JRCV Waveform Design and Optimization Formulation Design Methodology Optimization Problem for ACV Formulation of JRC Waveform Optimization Solution to the Optimization Problem JRC Algorithm Design Complexity Analysis Range-Doppler Processing Numerical Results Convergence Behavior of the JRC Algorithm Performance Assessment at the Radar Receiver Performance Assessment at the Communications Receiver Trade-Off Between Radar and Communications Conclusion References Appendix 9A Appendix 9B Appendix 9C Doppler-Tolerant Waveform Design Problem Formulation Optimization Method Extension of Other Methods to PECS Extension of MISL Extension of CAN Performance Analysis Norm Minimization Doppler-Tolerant Waveforms Comparison with the Counterparts Conclusion References Appendix 10A Waveform Design for STAP in MIMO Radars Problem Formulation Transmit Sequence and Receive Filter Design Optimum Filter Design Code Optimization Algorithm Discrete Phase Code Optimization Continuous Phase Code Optimization Numerical Results Conclusion References Cognitive Radar: Design and Implementation Cognitive Radar The Prototype Architecture LTE Application Framework Spectrum Sensing Application MIMO Radar Prototype Experiments and Results Performance Analysis Conclusion References Appendix 12A Appendix 12B Conclusion Computational Efficiency Waveform Diversity Performance Trade-Off About the Authors Index This book gives you a comprehensive overview of key optimization tools that can be used to design radar waveforms and adaptive signal processing strategies under practical constraints, helping you to meet the more and more stressing sensing system requirements. These include power method-like iterations, coordinate descent, and majorization-minimization, that are utilized for designing radar waveforms under practical constraints. The book walks you through how radar waveform synthesis is obtained as the solution to a constrained optimization problem such as finite energy, unimodularity (or being constant-modulus), and finite or discrete-phase (potentially binary) alphabet, which are dictated by the practical limitations of the real systems. Several approaches in each of these broad frameworks are detailed and various applications of these optimization techniques are described. Focusing on a holistic approach rather than a problem-specific approach, the book shows you what you need to effectively formulate waveform design and understand the flexibility of the framework for adapting to your own specific needs. You’ll have full access to the tools and knowledge you need to design waveform with optimized correlation/cross-correlation properties for SISO/SIMO and MIMO radars, taking into account spectral constraints for cognitive rads, as well as coexistence with communications and mitigate possible Doppler and quantization errors, and more. The book also includes representative software codes that further help you generate the described solutions. With its unique style of covering mathematical results along with their applications from diverse areas, this is a much-needed, detailed handbook for industry researchers, scientists and designers including medical, marine, defense, and automotive companies. It is also an excellent resource for advanced courses on radar signal processing This book gives you a comprehensive overview of key optimization tools that can be used to design radar waveforms and adaptive signal processing strategies under practical constraints, helping you to meet the more and more stressing sensing system requirements. These include power method-like iterations, coordinate descent, and majorization-minimization, that are utilized for designing radar waveforms under practical constraints. The book walks you through how radar waveform synthesis is obtained as the solution to a constrained optimization problem such as finite energy, unimodularity (or being constant-modulus), and finite or discrete-phase (potentially binary) alphabet, which are dictated by the practical limitations of the real systems. Several approaches in each of these broad frameworks are detailed and various applications of these optimization techniques are described. Focusing on a holistic approach rather than a problem-specific approach, the book shows you what you need to effectively formulate waveform design and understand the flexibility of the framework for adapting to your own specific needs. You0́9ll have full access to the tools and knowledge you need to design waveform with optimized correlation/cross-correlation properties for SISO/SIMO and MIMO radars, taking into account spectral constraints for cognitive rads, as well as coexistence with communications and mitigate possible Doppler and quantization errors, and more. The book also includes representative software codes that further help you generate the described solutions. With its unique style of covering mathematical results along with their applications from diverse areas, this is a much-needed, detailed handbook for industry researchers, scientists and designers including medical, marine, defense, and automotive companies. It is also an excellent resource for advanced courses on radar signal processing This book gives you a comprehensive overview of key optimization tools that can be used to design radar waveforms and adaptive signal processing strategies under practical constraints -- strategies such as power method-like iterations, coordinate descent, and majorization-minimization – that help you to meet the more and more stressing sensing system requirements. The book walks you through how radar waveform synthesis is obtained as the solution to a constrained optimization problem such as finite energy, unimodularity (or being constant-modulus), and finite or discrete-phase (potentially binary) alphabet, which are dictated by the practical limitations of the real systems. Several approaches in each of these broad frameworks are detailed and various applications of these optimization techniques are described. Focusing on a holistic approach rather than a problem-specific approach, the book shows you what you need to effectively formulate waveform design and understand the flexibility of the framework for adapting to your own specific needs. You’ll have full access to the tools and knowledge you need to design waveform with optimized correlation/cross-correlation properties for SISO/SIMO and MIMO radars, taking into account spectral constraints for cognitive rads, as well as coexistence with communications and mitigate possible Doppler and quantization errors, and more. The book also includes representative software codes that further help you generate the described solutions. With its unique style of covering mathematical results along with their applications from diverse areas, this is a much-needed, detailed handbook for industry researchers, scientists and designers including medical, marine, defense, and automotive companies. It is also an excellent resource for advanced courses on radar signal processing.