Radar Forward-Looking Super-Resolution Imaging Using a Two-Step Regularization Strategy

Regularization methods, including single constraint regularization and joint constraints regularization, have been applied to radar forward-looking super-resolution imaging. However, the ill-posedness of the antenna measurement matrix is serious, which degrades the imaging performance in low signal-to-noise ratio (SNR) conditions. In our work, the following two-step regularization strategy is proposed to achieve super-resolution imaging in low SNR conditions: 1) in the first step, a projection regularization method is designed to repair the ill-posed antenna measurement matrix by truncating and modifying singular values, which mitigates the ill-posedness of the deconvolution process and suppresses noise amplification and 2) in the second step, based on the repaired convolution model, the $L_{1}$ norm is introduced for sparse targets to improve the radar azimuth resolution. The iteratively reweighted norm solver is employed to solve the optimization problem. The superiority of the proposed two-step strategy is analyzed from the perspectives of singular value decomposition. The effectiveness of the proposed strategy is verified by simulated and experimental data.