A Bayesian Super-Resolution Method for Forward-Looking Scanning Radar Imaging Based on Split Bregman

摘要

In forward-looking scanning radar imaging, the azimuth resolution can be improved by adding the sparse constraint. However, the azimuth resolution is limited with noise influence by traditional sparse regularization methods. In this paper, we propose a Bayesian super-resolution method that solves the L₁ regularization problem using the split Bregman algorithm. This method decouples L₁ and L₂ norms for the independence of them to reduce the computational complexity. The simulations verify that the proposed algorithm provides a better resolution and de-noising ability compare with conventional methods.