Forward-Looking Super-Resolution Imaging Based on Echo Denoising and Noise Weighting at Low SNR

In forward-looking imaging radar sensors, the targets of our focus are typically sparse in comparison to the whole imaging scene, which provides the feasibility of applying compressed sensing (CS) to forward-looking super-resolution imaging. However, sparsity is not satisfied in radar imaging when there is strong noise. This will result in the emergence of false targets and affect imaging results when traditional CS-based approaches are employed directly. In this article, we propose a noise robust imaging method that combines echo denoising and noise weighting to solve this problem. First, the denoised radar echo is generated using a complex variational mode decomposition (CVMD) method. The CVMD method is an extension of variational mode decomposition (VMD), which has been used to the traditional real signal denoising. According to the signal spectrum characteristics, we successfully applied the proposed CVMD method to radar echo denoising. Second, a noise weighting method based on ${l}_{1}$ -norm is performed to further overcome strong noise. The weight comes from the CVMD denoising process, and it can better distinguish targets from the support area of noise. Lastly, simulated and measured data are utilized to verify the proposed method’s performance.