Scanning Radar Super-Resolution Imaging for Sea Surface Target Based on a Novel Observation Model

The super-resolution technique has gained substantial attraction among researchers aiming to address the challenge of forward-looking imaging. While recent studies have extended these super-resolution methods to sea surface targets, the focus has predominantly been on clutter distribution, with little attention to noise within the observation process. In our study, we firstly leverage prior information from both noise and clutter to redefine the observation model. Meanwhile, the noise and clutter probability distribution were united to construct the novel likelihood function. Then, the iterative threshold shrinkage algorithm has been utilized to solve the obtained likelihood function. Simulation experiments have verified the superiority of the proposed approach compared to conventional approaches.