Technique of Two-Dimensional Imaging Based on Regularization Method

Due to the influence of the uncertain factors, the motion compensation for inverse synthetic aperture radar(ISAR) target becomes more difficulty. Under high Signal-to-Noise Ratio(SNR) condition, the applicability and the quality of ISAR imaging will be improved through tranditional ways of envelope alignment and phase correction. When the SNR is lower, the commonly used methods receive certain restriction. Research shows that the radar target can be regarded as a few scatter centers at high frequency, which fits to the theory of sparse component. In this paper, a novel phase correction algorithm is presented, which uses sparse as global constraint condition. Experimental data and simulation results show that the proposed method converges faster and achieves super-resolution.