2026-10-14T00:18:09Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33981163 2026-09-24T10:06:43Z category_146 category_19 category_21 category_272 category_106 category_734 category_931 category_811 portal_87 item_type_3 month_year_09_2026

Sparse aperture ISAR imaging via nonconvex low-rank and sparsity regularization Ping Cheng (289811) Xuqian Dou (25103326) Jiaqun Zhao (25103329) Ziyu Wang (451367) Zheng Cao (562710) Space Science Pharmacology Biotechnology Environmental Sciences not elsewhere classified Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Inverse synthetic aperture radar (ISAR) sparse aperture low-rank sparsity nonconvex regularization alternating direction method of multipliers (ADMM) In inverse synthetic aperture radar (ISAR) imaging, traditional range-Doppler (RD) algorithm fails to get satisfactory imaging results in sparse aperture scenarios. Compressed sensing (CS) imaging methods are commonly used for sparse aperture radar imaging. However, only sparsity characteristic is used in CS-based methods. To improve imaging performance, other characteristics should be used. Range profile matrix exhibits low-rank characteristics as range profiles are similar during short ISAR imaging time, which can be employed in imaging. Therefore, a new joint sparse and low-rank imaging algorithm is proposed. 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 L1/2+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. 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.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.The effectiveness of the proposed method is verified by real ISAR data. Compared with L1/2+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. 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. 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. The effectiveness of the proposed method is verified by real ISAR data. Compared with L1/2+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. 2026-09-24T10:06:43Z Dataset Dataset 10.6084/m9.figshare.33981163.v1 https://figshare.com/articles/dataset/Sparse_aperture_ISAR_imaging_via_nonconvex_low-rank_and_sparsity_regularization/33981163 CC BY 4.0