Adaptive Detection of Multichannel Signals Exploiting Persymmetry

This book provides a comprehensive presentation of persymmetric adaptive detection, covering detector derivations, theoretical underpinnings, design methodology, and practical implementation techniques. It focuses on multichannel signal detection in Gaussian backgrounds and introduces new design approaches and techniques for efficient detector solutions in challenging sample-starved environments. It is of interest to students, scholars, and engineers in signal processing.

Format: Hardback
Length: 296 pages
Publication date: 20 December 2022
Publisher: Taylor & Francis Ltd

Introduction:
Modern radar systems are increasingly relying on multichannel data acquisition, where the received signals are vector-valued or even matrix-valued. This presents a significant challenge for signal detection in Gaussian backgrounds, as traditional single-channel detectors may not be able to effectively handle such complex data. Persymmetric adaptive detection is a promising approach to address this problem, as it allows for the exploitation of both the time and frequency domains of the signal.
Chapter 1:
In this chapter, the authors provide a comprehensive introduction to persymmetric adaptive detection. They begin by discussing the basic principles of adaptive detection, including the concept of the least mean squares (LMS) algorithm and its application to signal processing. They then introduce the concept of persymmetry, which refers to the use of two detectors with different orientations to capture the full range of signal characteristics.
Chapter 2:
The authors delve into the derivation of various persymmetric adaptive detectors, including the normalized least mean squares (NLMS) detector, the generalized eigenvalue decomposition (GED) detector, and the generalized likelihood ratio test (GLRT) detector. They provide detailed explanations of the mathematical models underlying each detector, as well as the design considerations and techniques involved in their implementation.
Chapter 3:
In this chapter, the authors discuss the theoretical underpinnings of persymmetric adaptive detection. They introduce the concept of the covariance matrix, which is used to describe the behavior of the signal in the time and frequency domains. They also discuss the concept of the Fisher information matrix, which is used to evaluate the performance of different detectors.
Chapter 4:
The authors present new design approaches and techniques for persymmetric adaptive detection. They introduce the concept of the asymmetric weighting function, which allows for the optimization of the detector performance in different signal-to-noise ratio (SNR) regimes. They also discuss the use of multiple detectors, which can be used to improve the detection performance in challenging sample-starved environments.
Chapter 5:
The authors discuss the practical implementation of persymmetric adaptive detection. They introduce the concept of the recursive least squares (RLS) algorithm, which is used to update the detector parameters in real-time. They also discuss the use of parallel processing techniques, such as the fast Fourier transform (FFT), to accelerate the computation of the detector parameters.
Chapter 6:
The authors present several case studies and examples of persymmetric adaptive detection in radar applications. They discuss the use of persymmetric adaptive detectors in target tracking, clutter suppression, and object classification. They also highlight the advantages and limitations of persymmetric adaptive detection in different scenarios.
Conclusion:
In conclusion, this book offers a comprehensive presentation of persymmetric adaptive detection, including detector derivations and the definition of key concepts. It provides a detailed discussion of the theoretical underpinnings, design methodology, design considerations, and techniques enabling its practical implementation. The book will be of interest to students, scholars, and engineers in the field of signal processing, especially those who have a solid background in statistical signal processing, multivariate statistical analysis, matrix theory, and mathematical analysis.

Weight: 632g
Dimension: 161 x 241 x 26 (mm)
ISBN-13: 9781032374246