Radar Forward-Looking Super-Resolution Imaging Using a Two-Step Regularization Strategy
Xingyu Tuo, Deqing Mao, Yin Zhang, Yongchao Zhang, Yulin Huang, Jianyu Yang
Abstract
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{1}$</tex-math></inline-formula> 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.