Joint Sparsity-Based Compressive Sensing for Improved Target Detection in Multistatic SAR Imaging

This study explores the application of a joint sparse recovery compressive sensing algorithm to the multistatic synthetic aperture radar (SAR) imaging and efficient target identification. An approximate SAR image reconstruction algorithm is proposed for the TanDEM mode operation to improve the target recovery and noise suppression by applying SMV-CS and MMV-CS. The feasibility of the proposed algorithm was evaluated using simplified multistatic SAR simulations. For validation, an experimental raw data has been produced by using vehicle-based multistiatic SAR test-bed. It is demonstrated that integrating multistatic SAR images with the MMV-CS algorithm can be used for the enhanced target identification. This study highlights the potential benefits of multi-platform SAR operation systems such as satellite constellations for improved target detection and identification.