Radar forward-looking super resolution imaging based on block sparse reconstruction with TSVD

In response to the problem of traditional bayesian deconvolution methods failing to effectively utilize the block structure information of the area targets, and resulting in poor imaging performance for area targets. we apply the pattern-coupled sparse Bayesian learning (PCSBL) method to forward-looking imaging in this paper. Moreover, in response to the problem of rapid degradation of resolution in this method at low signal to noise ratio(SNR), we propose to use the truncated singular value decomposition (TSVD) method as the preprocessing before deconvolution, which can effectively suppress the noise amplification in the deconvolution process by discarding the small singular values of convolution matrix. Simulation results show that the proposed method still has good super-resolution imaging effect under low SNR. © 2023 Chinese Institute of Electronics. All rights reserved.