The Multi-domain Union Clutter Suppression Algorithm Based on Robust Principal Component Analysis

Abstract

In through-the-wall imaging, the clutter cannot be eliminated completely through traditional algorithms, and seriously affects the subsequent target detection and recognition. To solve the problem, based on robust principal component analysis theory, a joint low-rank and sparse model is established in echo and image domain respectively. The models are solved by smoothing fast alternating linearization method. Then, the target images are dealt with exponentially weighted multiply multi-domain image fusion to obtain the final image. The simulation results indicate that the algorithm has great speed and accuracy with effective improvement on imaging quality of targets.