The analytic solution of sparse signal reconstruction algorithm based on L1 regularization is a biased estimation, which leads to the underestimation of target intensity when applied to sparse SAR imaging, resulting in the bias effect and affecting the reconstruction accuracy. In this paper, we quantitatively analyze the bias effect in SAR imaging applications, and analyse the influence of target intensity, signal-to-noise ratio, intensity ratio of adjacent targets in the observation scene on the reconstruction bias. In order to suppress the bias effect and improve the reconstruction accuracy, we adopt a class of algorithms based on nonconvex penalty, and verify the performance of these algorithms using simulations and real data.
In this paper, we develop a group sparsity based wide angle synthetic aperture radar (WASAR) imaging model and propose a novel algorithm called backprojection based group complex approximate message passing (GCAMP-BP) to recover the anisotropic scene. Compare to conventional backprojection based complex approximate message passing (CAMP-BP) algorithm for the recovery of isotropic scene, the proposed method accommodates aspect dependent scattering behavior better and can produce better imagery. Simulated and experimental results are presented to demonstrate the validity of the proposed algorithm.
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