Sparse models and sparse recovery for ultra-wideband SAR applications

Abstract

This paper presents a simple yet very effective time-domain sparse representation and associated sparse recovery techniques that can robustly process raw data-intensive ultra-wideband (UWB) synthetic aperture radar (SAR) records in challenging noisy and bandwidth management environments. Unlike most previous approaches in compressed sensing for radar in general and SAR in particular, we take advantage of the sparsity of the scene and the correlation between the transmitted and received signal directly in the raw time domain even before attempting image formation. Our framework can be viewed as a collection of practical sparsity-driven preprocessing algorithms for radar applications that restores and denoises raw radar signals at each aperture position independently, leading to a significant reduction in the memory requirement as well as the computational complexity of the sparse recovery process. Recovery results from real-world data collected by the U.S. Army Research Laboratory (ARL) UWB SAR systems illustrate the robustness and effectiveness of our proposed framework on two critical applications: 1) recovery of missing spectral information in multiple frequency bands and 2) adaptive extraction and/or suppression of radio frequency interference (RFI) signals from SAR data records.