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Sparse super-resolution method based on truncated singular value decomposition strategy for radar forward-looking imaging

Abstract. In recent years, many deconvolution methods have been proposed for radar forward-looking super-resolution imaging based on the sparse characteristic of the targets. However, most of the deconvolution methods will be invalid due to the illposed convolution matrix under a low signal-to-noise ratio (SNR). This paper proposes a radar forward-looking super-resolution imaging method for the sparse target in the low SNR, which is based on truncated singular value decomposition (TSVD) strategy. The convolution model is reconstructed through TSVD strategy, by which the illposed character of deconvolution is modified. First, through choosing the truncated parameter in a reasonable way, the noise amplification is restrained and the main information of the target is maintained by the TSVD technique. Then, the convolution model is reconstructed based on the result of TSVD. Third, an objective function is established as the L1 constraint based on the regularization strategy. Finally, due to the fast convergence and low computational complexity, the iteratively reweighted least square method is utilized to obtain the optimal solution of the objective function. The noise amplification is suppressed while the sparse characteristic is utilized to improve the resolution. Hence, the false target is avoided and the locations of the targets are accurately recovered by the proposed method. The simulations and experimental results demonstrate that the proposed method is superior to the conventional sparse deconvolution method when the SNR is low.