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.

• 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