Weijian Liu

This book systematically presents adaptive multichannel signal detection in three types of non-ideal environments, including sample-starved scenarios, signal mismatch scenarios, and noise plus subspace interference environments. The authors provide definitions of key concepts, detailed derivations of adaptive multichannel signal detectors, and specific examples for each non-ideal environment. In addition, the possible future trend of adaptive detection methods is discussed, as well as two further research points – namely, the adaptive detection algorithms based on information geometry, and the hybrid approaches that combine adaptive detection algorithms with machine learning algorithms. The book will be of interest to researchers, advanced undergraduates, and graduate students in sonar, radar signal processing, and communications engineering.

This book systematically presents adaptive multichannel signal detection in three types of non-ideal environments, including sample-starved scenarios, signal mismatch scenarios, and noise plus subspace interference environments. The authors provide definitions of key concepts, detailed derivations of adaptive multichannel signal detectors, and specific examples for each non-ideal environment. In addition, the possible future trend of adaptive detection methods is discussed, as well as two further research points – namely, the adaptive detection algorithms based on information geometry, and the hybrid approaches that combine adaptive detection algorithms with machine learning algorithms. The book will be of interest to researchers, advanced undergraduates, and graduate students in sonar, radar signal processing, and communications engineering.

This book offers a systematic presentation of persymmetric adaptive detection, including detector derivations and the definition of key concepts, followed by detailed discussion relating to theoretical underpinnings, design methodology, design considerations, and techniques enabling its practical implementation. The received data for modern radar systems are usually multichannel, namely, vector-valued, or even matrix-valued. Multichannel signal detection in Gaussian backgrounds is a fundamental problem for radar applications. With an overarching focus on persymmetric adaptive detectors, this book presents the mathematical models and design principles necessary for analyzing the behavior of each kind of persymmetric adaptive detector. Building upon that, it also introduces new design approaches and techniques that will guide engineering students as well as radar engineers toward efficient detector solutions, especially in challenging sample-starved environments where training data are limited. This book will be of interest to students, scholars, and engineers in the field of signal processing. It will be especially useful for those who have a solid background in statistical signal processing, multivariate statistical analysis, matrix theory, and mathematical analysis.

This book offers a systematic presentation of persymmetric adaptive detection, including detector derivations and the definition of key concepts, followed by detailed discussion relating to theoretical underpinnings, design methodology, design considerations, and techniques enabling its practical implementation. The received data for modern radar systems are usually multichannel, namely, vector-valued, or even matrix-valued. Multichannel signal detection in Gaussian backgrounds is a fundamental problem for radar applications. With an overarching focus on persymmetric adaptive detectors, this book presents the mathematical models and design principles necessary for analyzing the behavior of each kind of persymmetric adaptive detector. Building upon that, it also introduces new design approaches and techniques that will guide engineering students as well as radar engineers toward efficient detector solutions, especially in challenging sample-starved environments where training data are limited. This book will be of interest to students, scholars, and engineers in the field of signal processing. It will be especially useful for those who have a solid background in statistical signal processing, multivariate statistical analysis, matrix theory, and mathematical analysis.