A Structural Sparse ISAR Imaging Method With Joint Phase Autofocusing

Aiming at improving the performance of sparse aperture inverse synthetic aperture radar (ISAR) imaging under the condition of phase error, an efficient structural sparse imaging algorithm with joint phase autofocusing is proposed in this letter. First, an azimuth sparse ISAR imaging model containing phase error is constructed. To fully utilize the structural sparse characteristics of the imaging target, the above imaging model is further transformed into an $\boldsymbol {l}_{1}$ norm optimization problem using structural weighting. Second, leveraging fast iterative shrinkagethresholding algorithm (FISTA), the phase error estimation and the target structure weight updating are integrated into the image reconstruction framework. By solving this compound optimization problem iteratively, the final high-resolution ISAR imaging results are obtained. Finally, the experimental results of the measured data show that the proposed algorithm can achieve well-focused image efficiently under phase error conditions and has a remarkable imaging performance under low signal-to-noise ratio (SNR) and sparse aperture conditions due to the utilization of the sparse structure of the target.