Sparse aperture ISAR imaging via nonconvex low-rank and sparsity regularization

To achieve superior imaging results, log-sum based nonconvex regularization rather than convex regularization is employed. Notably, low-rank and sparse problems based on log-sum regularization have closed-form solutions. Aternating direction method of multipliers (ADMM) algorithm is employed to solve the joint problem efficiently.

Experimental results on measured data of Yak42 aircraft demonstrate that, compared with traditional methods, the proposed method achieves better imaging results in much less time. The imaging time is reduced by one to two orders of magnitude compared with L_1/2+MC and SL0_LRLMS methods, and is less than half of that required by the convex regularization method. Therefore, the proposed method is an efficient sparse aperture ISAR imaging method with high imaging performance.</p> <p></p><p>To improve sparse aperture ISAR (inverse synthetic aperture radar) imaging performance, a nonconvex sparsity and low-rank method is proposed, which can break through the limitations of convex methods.</p><p>Log-sum function is employed as a nonconvex regularizer in the proposed method due to its excellent characteristics, which can not only improve imaging efficiency greatly but also improve image quality remarkably.</p><p>The effectiveness of the proposed method is verified by real ISAR data.

Compared with L_1/2+MC and SL0_LRLMS algorithms, the proposed method improves the imaging speed by two orders of magnitude. Compared with the convex regularization method, its imaging efficiency is improved by more than two times.</p><p></p> <p>To improve sparse aperture ISAR (inverse synthetic aperture radar) imaging performance, a nonconvex sparsity and low-rank method is proposed, which can break through the limitations of convex methods.</p> <p>Log-sum function is employed as a nonconvex regularizer in the proposed method due to its excellent characteristics, which can not only improve imaging efficiency greatly but also improve image quality remarkably.</p> <p>The effectiveness of the proposed method is verified by real ISAR data.

Compared with L_1/2+MC and SL0_LRLMS algorithms, the proposed method improves the imaging speed by two orders of magnitude. Compared with the convex regularization method, its imaging efficiency is improved by more than two times.</p>