WASAR Sparse Imaging Method Based on Low-Rank Structures

‍ ‍When the synthetic aperture in azimuth exceeds a certain angle range， the electromagnetic characters of observed targets generally represent anisotropic in synthetic aperture radar （SAR） imaging. And it leads to the failure of the conventional SAR imaging methods， which are derived from the hypothesis of isotropic targets. By dividing the wide aperture into some subapertures， the wide angle synthetic aperture radar （WASAR） can use the echo data in subapertures to achieve radar imaging of the targets from different angles. Since the scattering properties of radar targets are similar in the adjacent subapertures， which results in the similarity of the scattering-center distribution corresponding to every subaperture， the subaperture imaging results of WASAR have the similar support. And the specific property is called low-rank structures. In order to take advantage of the property， Karhunen Loeve （KL） transform is introduced into the imaging of WASAR. Meanwhile， we construct the WASAR sparse imaging model based on low-rank structures by the sparsity of the distribution of strong scattering centers. Then， augmented Lagrangian method and alternating direction method of multipliers are utilized to solve the model iteratively， so that the imaging results of WASAR can be obtained. Compared with the conventional WASAR imaging methods， the proposed algorithm in this paper not only improves the reconstruction precision of the backscattering coefficients of targets， but also suppresses the negative influence of sidelobe and noise on the image quality. The experimental results have also shown that it is able to recover the electromagnetic characters of targets in multiple angles better.