Advanced Sparsity-Driven Models and Methods for Radar Applications

by Gang Li

Overview

This book introduces advanced sparsity-driven models and methods and their applications in radar tasks such as detection, imaging and classification. Compressed sensing (CS) is one of the most active topics in the signal processing area. By exploiting and promoting the sparsity of the signals of interest, CS offers a new framework for reducing data without compromising the performance of signal recovery, or for enhancing resolution without increasing measurements.

An introductory chapter outlines the fundamentals of sparse signal recovery. The following topics are then systematically and comprehensively addressed: hybrid greedy pursuit algorithms for enhancing radar imaging quality; two-level block sparsity model for multi-channel radar signals; parametric sparse representation for radar imaging with model uncertainty; Poisson-disk sampling for high-resolution and wide-swath SAR imaging; when advanced sparse models meet coarsely quantized radar data; sparsity-aware micro-Doppler analysis for radar target classification; and distributed detection of sparse signals in radar networks via locally most powerful test. Finally, a concluding chapter summarises key points from the preceding chapters and offers concise perspectives.

The book focuses on how to apply the CS-based models and algorithms to solve practical problems in radar, for the radar and signal processing research communities.

Product Details

ISBN-13:	9781839530753
Publisher:	The Institution of Engineering and Technology
Publication date:	01/28/2021
Series:	Radar, Sonar and Navigation
Pages:	272
Product dimensions:	6.14(w) x 9.21(h) x (d)

About the Author

Gang Li is a Professor at the Department of Electronic Engineering, Tsinghua University, China. His research interests include radar signal processing, remote sensing, distributed signal processing, and information fusion. He has published over 150 papers on these subjects. He is a recipient of the National Science Fund for Distinguished Young Scholars of China and the Royal Society Newton Advanced Fellowship of United Kingdom. He is a Senior Member of the IEEE.

Table of Contents

• Chapter 1: Introduction
• Chapter 2: Hybrid greedy pursuit algorithms for enhancing radar imaging quality
• Chapter 3: Two-level block sparsity model for multichannel radar signals
• Chapter 4: Parametric sparse representation for radar imaging with model uncertainty
• Chapter 5: Poisson disk sampling for high-resolution and wide-swath SAR imaging
• Chapter 6: When advanced sparse signal models meet coarsely quantized radar data
• Chapter 7: Sparsity aware micro-Doppler analysis for radar target classification
• Chapter 8: Distributed detection of sparse signals in radar networks via locally most powerful test
• Chapter 9: Summary and perspectives