Advanced Sparsity-Driven Models and Methods for Radar Applications

Gang Li

ISBN 10: 1839530758 / ISBN 13: 9781839530753

Published by Institution Of Engineering & Technology Jan 2021, 2021

About this Item

Neuware - This book introduces advanced sparsity-driven models and methods and their applications in radar tasks such as detection, imaging and classification. It is based on research from the last decade, with a particular focus on applying compressed-sensing-based models and algorithms to solve practical problems in radar.

Seller Inventory # 9781839530753

Bibliographic Details

• Title: Advanced Sparsity-Driven Models and Methods for Radar Applications
• Publisher: Institution Of Engineering & Technology Jan 2021
• Publication Date: 2021
• Language: English
• ISBN 10: 1839530758
• ISBN 13: 9781839530753
• Binding: Buch
• Condition: Neu
• Dimensions: 239W x 163H x 18D millimeters 239 millimeters width by 163 millimeters height by 18 millimeters depth
• Weight: 590 grams

About this title

Synopsis

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.

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.