Compressed Sensing in Radar Signal Processing

Antonio De Maio; Yonina C. Eldar; Alexander M. Haimovich

doi:https://doi.org/10.1017/9781108552653

Compressed Sensing in Radar Signal Processing

Antonio De Maio (Editor), Yonina C. Eldar (Editor), Alexander M. Haimovich (Editor)

Department of Computer Science and Applied Mathematics

Weizmann Institute of Science

Research output: Book/Report › Book

Abstract

This book aims to present the latest theoretical and practical advances in radar signalprocessing using tools from CS. In particular, this book offers an up-to-date review offundamental and practical aspects of sparse reconstruction in radar and remote sensing,demonstrating the potential benefits achievable with the CS paradigm. We take a widerscope than previous edited books on CS-based radars: we do not restrict ourselves tospecific disciplines (such as earth observation as in [4]) or applications (such as urbansensingasin[5]), but discuss a variety of diverse application fields, including clutterrejection, constant false alarm rate (CFAR) processing, adaptive beamforming, randomarrays for radar, space–time adaptive processing (STAP), multiple input multiple output(MIMO) systems, radar super-resolution, cognitive radar [6] applications involving sub-Nyquist sampling and spectrum sensing, radio frequency interference (RFI) suppres-sion, and synthetic aperture radar (SAR).

Original language	English
Publisher	Cambridge University Press
Number of pages	358
ISBN (Electronic)	9781108552653
ISBN (Print)	9781108428293
DOIs	https://doi.org/10.1017/9781108552653
State	Published - Sep 2019

All Science Journal Classification (ASJC) codes

General Engineering
General Computer Science

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https://doi.org/10.1017/9781108552653

Cite this

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Compressed Sensing in Radar Signal Processing

Abstract

All Science Journal Classification (ASJC) codes

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