Robust and Efficient Sparse-feature Enhancementfor Generalized SAR Imagery

For the problem of sparse feature enhancement in Synthetic Aperture Radar (SAR) imagery, conventional methods are difficult to achieve a preferable balance between accuracy and efficiency. In this paper, a robust and efficient SAR imaging algorithm based on Complex Alternating Direction Method of Multipliers(C-ADMM) is proposed for general SAR imaging feature enhancement within complex raw data domain. The problem is firstly imposed by an augmented Lagrange function, and the complex ${\ell _1}$-norm of the intended SAR image is jointly formulated within the C-ADMM framework. Then, the proximal mapping of the sparse feature is derived as a soft-thresholding operator. Further, an iterative processing procedure is designed according to Gaussian-Deidel principle, and the convergence of the proposed algorithm is analyzed. In the experiment, the performance of the proposed algorithm is firstly examined by the simulated data in terms of Phase Transition Diagram (PTD) under different under-sampling rate and degree of sparsity. Then, various raw SAR and Inverse SAR(ISAR) data, for both stationary ground scene and Ground Moving Target Imaging(CMTIm), are applied to further verifying the proposed C-ADMM, and comparisons with classical Convex(CVX) and Bayesian Compress Sensing(BCS) algorithms are performed, so that both the effectiveness and superiority of the C-ADMM algorithm can be verified.