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.
