JSUnet: A New Hybrid U-shaped Network for Jamming Suppression

Abstract

High quality of synthetic aperture radar (SAR) images is an essential requirement for many applications. However. the increasingly intensive jamming deteriorated the imaging quality. It is therefore needed to achieve jamming suppression. The traditional methods were the empirical model parameterized by simple function, such as adaptive filtering and subspace projection. These methods usually relied on the detail jamming parameters to discriminate the jamming and the signal. To address these problems, a new hybrid U-shaped network (JSUnet) is proposed in this article. First, JSUnet was constructed, which is a hybrid U-shaped network of CNN and Transformer. Second, open source SAR data was obtained and used to build the dataset. Third, a supervised training was designed to train JSUnet. Finally, the trained JSUnet was used to complete the jamming suppression of interfered SAR images end-to-end. Experiments demonstrated the powerful performance of the proposed method and compared it with traditional filtering method and other deep learning-based methods. The proposed method achieved the state- of-the-art result.