Clutter Suppression in Passive ISAR Using Compressive Sensing

This article proposes a method to remove clutter in passive bistatic inverse synthetic aperture radar (ISAR) based on low-rank and sparse matrix recovery algorithms. Previously, the clutter removal process was done mainly in the preprocessing stages. Still, our clutter removal and ISAR processing are done concurrently using the low-rank plus sparse model. It is shown that the proposed method can effectively remove both stationary and nonstationary clutter from ISAR images while maintaining information on slow-moving targets. Also, given the knowledge of the clutter spectrum, one can use this information to remove clutter more effectively. The superior performance of the proposed technique is validated using computer simulation and experimental data.