Sparse Imaging of Space Targets Using Kalman Filter

In view of the excellent signal estimation performance of the Kalman Filter (KF), combining the KF algorithm with the greedy algorithm and an imaging method is presented for Inverse Synthetic Aperture Radar (ISAR) using KF with sparse constraints. Large space targets including the targets having large-size components and long imaging time may introduce the Migration Through Resolution Cell (MTRC) and quadratic phase modulation in the cross-range. The MTRC correction is firstly performed. Then, the observation matrix is constructed by including the quadratic phase term. By maximizing the image sharpness, an estimation of the target angular velocity as well as a well-focused image can be obtained. The estimated angular velocity can be further used for image cross-range scaling. The processing of the simulated satellite ISAR data verifies the effectiveness of the presented imaging processing method. The image quality is superior to the traditional Range Doppler (RD) method and Orthogonal Matching Pursuit (OMP) method.