Low Rank Approximation Approach for Microwave Radar Coincidence Imaging

A low rank approximation algorithm for the Microwave Radar Coincidence Imaging (MRCI) is proposed in this paper. As a novel method of the radar imaging, the MRCI has the characteristics of high resolution and strong antiinference. Nevertheless, the imaging result will be aggravated when the Signal Noise Ratio (SNR) is low due to the illcondition of the reference matrix. Hence, a low rank approximation approach combined with conventional sparse reconstruction algorithm is proposed to improve the performance of the MRCI in the low SNR environment. First, the reference matrix is decomposed into the product of two matrices and the ranks of two matrices are analyzed. Then the low rank approximation approach is adopted and a new approximate imaging equation is estimated. Combined with SBL algorithm, the performance of reconstructed image is improved especially when the SNR is low. Simulation results are presented to validate the whole analysis and the proposed approach.