High-Squint Sparse Super-Resolution Imaging for Airborne Scanning Radar Based on Likes

High-squint super-resolution imaging for airborne scanning radar is crucial in remote sensing and earth information observation. Various methods have been proposed to enhance the azimuth resolution of imaging. However traditional methods are often limited by the requirement for manual adjustment of hyperparameters. In this paper, we propose a hyperparameter-free high-squint super-resolution method for airborne scanning radar based on likelihood based estimation of sparse parameters (LIKES). Compared to traditional sparse imaging methods, our presented approach ensures super-resolution while addressing the issue of manual hyperparameter adjustment. Simulation results demonstrate the effectiveness of the proposed method.