A Super-Resolution Imaging Method for Real-Aperture Scanning Radar Based on MRF Prior Model

Abstract

Deconvolution technology can be utilized to improve the angular resolution of real-aperture scanning radar (RASR) with high efficiency and low cost. However, it is an ill-posed problem and the solution is sensitive to noise. Regularization methods are considered to be efficient ways to ease the noise sensitivity by absorbing the prior information into the objective function. In this paper, we propose a new super-resolution imaging method for RASA based on the Markov random field (MRF). Compared with the published angular super-resolution methods for RASA, the proposed method takes advantage of the two-dimensional spatial prior information and can recover the shape of scene much better. Simulations are carried out to demonstrate the effectiveness of the proposed method.