Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing. Issue 3 (6th January 2022)

• Record Type: Journal Article
• Authors: Mingjiu, Lv; Lei, Ma; Jianchao, Ma; Wenfeng, Chen; Jun, Yang; Xiaoyan, Ma; Qi, Cheng
• Is Part Of: IET signal processing. Volume 16:Issue 3(2022)
• Page Start: 310
• Page End: 326
• Publication Date: 2022-01-06
• Subjects: radar imaging -- radar resolution -- radar signal processing; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/sil2.12092
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract: Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super‐resolution imaging, multi‐target resolution, and cognitive reconfiguration. This paper proposes a fast, super‐resolution imaging method employing continuous compressive sensing for sparse‐aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low‐rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super‐resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super‐resolution image and shows better performance than state‐of‐the‐art methods.