Low-Rank and Norm Regularization Method for Imaging From Coated Scatterers

With the development of radar system, there are high demands to perform inverse synthetic aperture radar (ISAR) imaging from sparse frequency bands and sparse aperture data. The compressive sensing (CS)-based and matrix completion (MC)-based methods could be used for analyzing complex targets such as ISAR images of coated scatterers. Compared with the perfect electric conductor (PEC) target, complex targets tend to be more noisy and more diverse in image representation, which brings greater initial value offset when calculating the corresponding optimization goals, thus limiting the performance and effectiveness of the general optimization methods. In this work, we first construct the ISAR-coated imaging model from the aspects of PEC scatterer, and then, a fast low-rank MC and norm regularization (MC + NR) algorithm is proposed for high-resolution coated ISAR sparse imaging, which can effectively preserve target features while maintaining sparsity. The experiments using simulated data demonstrate that the proposed method converges over five times faster than norm regularization method and can obtain a better sparse imaging result as well.