Fast two‐dimensional sparse signal gridless recovery algorithm for MIMO array SAR 3D imaging. Issue 9 (15th July 2020) - Wu, Chunxiao; Zhang, Zenghui; Yu, Wenxian
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.