Sparse Aperture Inverse Synthetic Aperture Radar Imaging Based on Gridless Compressive Sensing

Under the condition of sparse aperture, the traditional inverse synthetic aperture radar (ISAR) imaging method suffers from the sidelobe. Compressive sensing (CS) is a method to solve this problem. The traditional CS algorithm assumes that the strong scattering point of the target is located on the grid. Yet in practice, the distribution of the scattering point is continuous. If it deviates from the grid, the performance of CS will deteriorate. Therefore, a sparse aperture ISAR imaging method based on gridless CS is proposed in this paper. The method does not need to discretize the scene and use atomic norm minimization as sparse constraint. By solving semidefinite program, the position of scattering centers can be estimated directly in continuous space, which can solve the problem of basis mismatch effectively. The simulation results validate the feasibility of this algorithm.