ISAR Imaging With Phase Distortion and 2-D Compressive Sampling

Sparse sampling technique has important applications in modern measurement field, especially in inverse synthetic aperture radar (ISAR) imaging. However, the phase distortion of the signal and the uncertainty of the sparse patterns and sparse rates often significantly affect the reconstruction accuracy, thus limiting the restoration and recognition of target details. For this problem, we propose a new approach that incorporates phase correction into the sparse ISAR imaging model guided by the principles of low rank and sparsity. Subsequently, the alternating direction method of multipliers (ADMM) is employed to handle the subproblems, including the sparse recovery and phase correction. In particular, the proposed method avoids matrix vectorization and nested iterations to increase computational efficiency. Finally, simulated and real-world experiments are conducted to demonstrate the robustness of the method in producing sharper ISAR images with reduced noise and sidelobes across various phase errors, signal-to-noise ratios (SNRs), and compressive sampling patterns.