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.
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