Azimuth Superresolution of Forward-Looking Radar Imaging Which Relies on Linearized Bregman

Abstract

Forward-looking radar plays an important role in many military and civilian fields. However, the problem of low azimuth resolution has restricted its applications seriously. Although many methods have been used to achieve azimuth superresolution, the traditional methods suffer from noise amplification or limited resolution under low signal-to-noise (SNR) condition. In this paper, we proposed a Bayesian deconvolution method which relies on linearized Bregman to achieve azimuth superresolution of forward-looking radar imaging. We first used the complex Gaussian distribution and Laplace distribution to describe the distribution of noise and targets, respectively, and transformed the superresolution problem into a convex optimization problem by maximum a posteriori estimation in the Bayesian framework. Second, linearized Bregman algorithm was used to solve the convex optimization problem. The proposed method introduces the prior information of noise and target, and overcomes the ill-posedness of deconvolution. As a result, the azimuth resolution is remarkably enhanced. Besides, the proposed method has high computational efficiency by linearizing objection function, so it can take both time cost and resolution improvement into consideration. Finally, the superior performance was verified by simulation and experimental data.