ISAR 2-D Imaging under Low SNR Based on Improved Compressive Sensing

Under the target's sparse prior condition, the sparse signal recovery algorithm based on compressive sensing (CS) theory can improve the imaging resolution of the inverse synthetic aperture radar (ISAR) with limited observation samples. However, traditional sparse signal recovery algorithm has poor robustness under low SNR. In view of this problem, under the two-dimensional (2-D) ISAR signal model, a novel ISAR 2-D imaging algorithm under low SNR based on improved compressive sensing is proposed in this paper. The low rank property of ISAR echo is utilized to reduce the adverse effects of noise to the conventional sparse-based algorithm and enhance the robustness of ISAR sparse imaging algorithm. The performance of proposed method is demonstrated by simulation and real experimental data.