MTRC Compensation for Sparse Aperture ISAR Imaging

With the improvement of the resolution of radar system, the migration through resolution cells (MTRC) in inverse synthetic aperture radar (ISAR) imaging becomes more and more serious. It can be well compensated for the complete data. For the sparse aperture (SA) data, however, the compensation of MTRC is still a challenge. In this paper, a novel MTRC compensation algorithm for sparse aperture ISAR (SA-ISAR) imaging is proposed. Specifically, the MTRC compensation for SA-ISAR imaging is modeled as a sparse signal recovery problem, in which the sparse signal is modeled by the Laplacian scale mixture (LSM) prior and reconstructed by the expectation maximization based variational Bayesian inference (EM-VB) algorithm. Experimental results confirm the effectiveness of the proposed algorithm.