A Sparsity-Assisted Minimum-Entropy Autofocus Algorithm for SAR Moving Target Imaging

To address the slow convergence and sensitivity to a low signal-to-noise ratio (SNR) of the minimum-entropy autofocus (MEA) algorithm in the refocusing of moving targets, this paper proposes a sparsity-assisted minimum-entropy autofocus algorithm. Within the framework of the traditional gradient descent MEA with variable step size, the proposed method introduces soft-thresholding-based sparse reconstruction to make moving targets more prominent and suppress background clutter in the image domain. A joint metric combining image entropy and the Hoyer sparsity measure is then constructed, and a three-point adaptive, variable step-size search is employed to reduce the number of evaluations of the cost function, thereby effectively mitigating clutter interference and significantly accelerating the optimization while maintaining good focusing quality. Simulation and real-data experiments demonstrate that, under complex phase errors and different SNR conditions, the proposed algorithm outperforms the conventional variable-step MEA in terms of image entropy, image sparsity, and runtime, while keeping the phase error estimation accuracy within a small range. These results indicate that the proposed method can achieve satisfactory moving-target focusing performance and exhibits promising engineering applicability.