A Fast and Accurate Compressed Sensing Reconstruction Algorithm for ISAR Imaging

Compressed sensing (CS) has provided a novel way for inverse synthetic aperture radar (ISAR) imaging. In CS based ISAR imaging, the continuous range-Doppler plane is divided into grids, and the strong scattering points are assumed on the grids. However, the strong scattering points may not be on the grids, which will degrade the performance of CS greatly. This is the off-grid problem. To solve the problem, most of existing methods aim at estimating off-grid error and sparse solution jointly. But the computational cost is relatively high. To reduce the computational cost, a fast and accurate algorithm has been proposed in this paper. Interestingly, the joint optimization problem can be solved efficiently through two least squares problems based on first order Taylor approximation. When applied into simulated chirp signal and quasi real ISAR data, the proposed algorithm has got much better imaging results than existing algorithms. Therefore, it is a promising off-grid CS based ISAR imaging algorithm.