Compressive Sensing for radar STAP

We present a technique that merges Compressive Sensing and Wiener filtering (denoted as CS-Wiener) and compares it to standard Wiener filtering (denoted as Std-Wiener) in radar detection and estimation in a Space-Time Adaptive Processing application. The method leverages an inherent similarity in the Generalized Sidelobe Canceller transformation to that of the Compressive Sensing sampling function, namely its broadband, random or `white´ characteristic. We illustrate a data sampling reduction factor of 84% by using a CS-Wiener algorithm as compared to Std-Wiener approaches, while achieving approximately identical performance in SINR and subsequent radar detection performance.