Regularization Method with Weak-Depended on Parameter for Forward-Looking Super-Resolution Imaging

Currently, regularization methods are widely applied to radar forward-looking super-resolution imaging, but imaging performance is greatly affected by the regularization parameter. In order to address this issue, the regularization method with weak-depended on parameter for forward-looking super-resolution imaging is proposed in our work. First, the objective function is established under the premise of sparse target prior; then, iteratively reweighted solver is applied to resolve the objective function. The key idea is to join the regularization weighting factor in the process of solving sparse regularization problem, which reduces the sensitivity to the regularization parameter and avoids the imaging error caused by unreasonable parameter selection. Compared to traditional sparse regularization method, the proposed method is less dependent on the regularization parameter, and the imaging performance is superior under the same conditions. Simulation results verify the effectiveness of the proposed method.