A Parameter-Free Estimation Method Based on Low-Rank and Sparse Hybrid Constraints for Scanning Radar Forward-Looking Imaging

Super-resolution techniques based on the convolution model of target scattering coefficient and antenna pattem have been widely used in scanning radar forward-looking imaging for past few years. In previous work, the sparse regularization model is proved to be effective in solving the recovery problem of sparse scenes. However, traditional regularization methods usually only add constraints to the target and the regularization parameters are difficult to choose, thus easily causing noise amplification and image recovery distortion. In this paper, a parameter-free estimation method based on low-rank and sparse hybrid constraints is proposed. Firstly, based on the traditional regularization model, sparse and low-rank constraints are added to the target and background respectively to effectively suppress the noise amplification; then, a parameter-free estimation solver is proposed to solve the problem that the regularization parameter is much more difficult to choose. In addition, the superior performance of the proposed method is verified by simulations.