Fast two-dimensional sparse signal gridless recovery algorithm for MIMO array SAR 3D imaging

• Published in: IET radar, sonar & navigation Vol. 14; no. 9; pp. 1370 - 1381
• Main Authors: Wu, Chunxiao, Zhang, Zenghui, Yu, Wenxian
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.09.2020
• Subjects: 2D compressive sensing; 2D GLR algorithm; 2D imaging signal model; antenna array; antenna arrays; azimuth‐cross‐track plane image; compressed sensing; fast 2D gridless recovery; fast two‐dimensional; fixed grid points; illuminated scene; image reconstruction; image resolution; length limitations; MIMO array SAR 3D imaging; MIMO communication; multiple‐input multiple‐output array synthetic aperture radar; radar imaging; real‐domain based 2D sparse signal gridless reconstruction approach; Research Article; signal models; single track; super‐resolution algorithms; synthetic aperture radar; three‐dimensional imagery; compressed sensing; azimuth-cross-track plane image; three-dimensional imagery; single track; 2D compressive sensing; fast 2D gridless recovery; MIMO array SAR 3D imaging; multiple-input multiple-output array synthetic aperture radar; antenna arrays; radar imaging; image resolution; MIMO communication; antenna array; real-domain based 2D sparse signal gridless reconstruction approach; fixed grid points; length limitations; fast two-dimensional; super-resolution algorithms; signal models; image reconstruction; illuminated scene; 2D imaging signal model; CS; synthetic aperture radar; 2D GLR algorithm
• ISSN: 1751-8784; 1751-8792
• DOI: 10.1049/iet-rsn.2020.0065

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.