Superresolution Composite SAR Imaging Method by Hierarchical Pattern With Attributed Scattering Priors

Compared with the ideal point scattering model, the attributed scattering center (ASC) model provides concise and physically relevant description of the man-made targets, such as the vehicles and ships, giving an efficient way to interpret the measurements of high-frequency and wide-angle synthetic aperture radar (SAR). Since the ASC model is complicated, it gives rise to a high-dimensional model parameter estimation problem, leading to a heavy computational burden. Aimed at this problem, in this article, a novel superresolution composite SAR imaging algorithm is proposed based on sparse representation, in which the ASC attributes are estimated by a hierarchical pattern for reducing the computational cost. In the proposed algorithm, the ASC model parameters, classified into three categories, are estimated coarsely via minimum variance pursuit (MVP). Then these coarse estimates are refined jointly using the consensus alternating direction method of multipliers (CADMM) optimization for polarimetric measurements. The spectrum extrapolation and replacement are performed for superresolution and reducing the impact of model mismatch, followed by a fast Fourier transform (FFT)-based operation to efficiently obtain the final superresolution image. The proposed approach not only possesses the ability of superresolution reconstruction with the target nonideal scattering features' enhancement but also offers the physically relevant attributes on the premise of estimation accuracy improvement and calculation capacity reduction. Extensive experiments are conducted to corroborate the effectiveness of the proposed algorithm.