Forward-Looking Radar Super-Resolution Imaging Method Based on L2-Norm and Sparse-TV Constraint

The super-resolution method can effectively improve the azimuth resolution of forward-looking scanning radar. However, the super-resolution technology is highly sensitive to noise, especially prone to noise amplification during the solution process. To address this issue, a radar forward-looking super-resolution imaging method based on L2-norm and sparse-total variation (STV) constraint is proposed in this paper. This novel method constructs the objective function through combining the L2-norm and the STV penalty, which can stabilize the solution process of the STV algorithm through making use of the insusceptibility of the L2-norm to noise. Then, the projection on convex sets (POCS) algorithm is used to solve the obtained objective function. Experimental results demonstrate that the proposed method can effectively suppress noise amplification while maintaining outstanding imaging quality.