Fast two‐dimensional sparse signal gridless recovery algorithm for MIMO array SAR 3D imaging. Issue 9 (15th July 2020)

• Record Type: Journal Article
• Authors: Wu, Chunxiao; Zhang, Zenghui; Yu, Wenxian
• Is Part Of: IET radar, sonar & navigation. Volume 14:Issue 9(2020)
• Page Start: 1370
• Page End: 1381
• Publication Date: 2020-07-15
• Subjects: radar imaging -- antenna arrays -- image resolution -- image reconstruction -- compressed sensing -- synthetic aperture radar -- MIMO communication; fast two‐dimensional -- MIMO array SAR 3D imaging -- multiple‐input multiple‐output array synthetic aperture radar -- three‐dimensional imagery -- illuminated scene -- single track -- length limitations -- antenna array -- super‐resolution algorithms -- 2D compressive sensing -- CS -- azimuth‐cross‐track plane image -- fixed grid points -- fast 2D gridless recovery -- 2D imaging signal model -- signal models -- real‐domain based 2D sparse signal gridless reconstruction approach -- 2D GLR algorithm; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2020.0065
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Multiple‐input multiple‐output (MIMO) array synthetic aperture radar (SAR) can be used to directly obtain the three‐dimensional (3D) imagery of the illuminated scene with a single track. Due to the length limitations of synthetic aperture and antenna array, the super‐resolution algorithms within the framework of 2D compressive sensing (CS) have been conceived to reconstruct the azimuth‐cross‐track plane image because of its spatial sparsity. Since the desired scatterers are presupposed to be distributed over a series of fixed grid points, the location accuracy of the existing 2D CS algorithms is relatively low. To overcome this problem, a fast 2D gridless recovery (GLR) algorithm for the 2D imaging signal model established in the real domain is proposed in this study. First, two different forms of 2D real‐valued signal models with uniform or random sampling on the azimuth‐cross‐track plane are reconstructed by means of unitary transformation. Further, the real‐domain based 2D sparse signal gridless reconstruction approach is derived. Finally, extensive simulation results validate that the proposed 2D real‐valued GLR approach can approximately improve the computational efficiency by a factor of ten in terms of CPU time when compared with that of the 2D GLR algorithm in the complex domain.