DLSLA 3-D SAR Imaging Based on Reweighted Gridless Sparse Recovery Method

Downward-looking sparse-linear-array 3-D synthetic aperture radar (DLSLA 3-D SAR) cross-track reconstruction usually suffers from incomplete observation and limited resolution. The incomplete observation is caused by the sparse and nonuniform distribution of the equivalent antenna phase centers (APCs) due to the array elements' installation location restriction, loss, or deviation. Sparse recovery methods provide a solution with improved resolution from the incomplete observation for the 3-D imaging scene that behaves with spatial sparsity. However, conventional grid-based sparse recovery (GB-SR) methods are under the assumption that the scatterers are located on the discretized grids; otherwise, the off-grid effect or basis mismatch problem will occur. In this letter, we propose a reweighted scheme-based gridless sparse recovery (GL-SR) method, i.e., reweighted gridless sparse iterative covariance-based estimation (RGLS), for DLSLA 3-D SAR cross-track imaging. The proposed method possesses the merits of gridless SPICE (GLS), i.e., free of off-grid effect and user parameters, and has a statistically more appealing property than GLS by adopting the reweighted scheme. As seen from the experiments that compare the performance of GB-SR and GL-SR methods for DLSLA 3-D SAR cross-track reconstruction, the proposed method performs outstandingly under the circumstance of sparse and nonuniform APCs' distribution.