Learning Time–Frequency Information With Prior for SAR Radio Frequency Interference Suppression

Abstract

In the complex electromagnetic environment, radio frequency interference (RFI) from other radiation sources often conflicts with synthetic aperture radar (SAR) systems, which overlaps and destroys the useful data in the same frequency band, causing adverse impact to the quality of SAR imaging. When faced with wideband or mixed complicated RFI, traditional methods inevitablely damage the original signal, and cannot effectively protect and reconstruct the useful information. Besides, the current semi-parametric algorithms have large computations and limited generalization ability. To address these issues, this paper proposes a prior-induced-learning framework (PISNet) to achieve RFI suppression and useful signal recovery in time-frequency domain. Both narrowband and wideband interference are uniformly modeled as a sparse distribution in time-frequency domain, and the stationarity of SAR echoes determines its low-rank characteristic. These properties of RFI and SAR data are treated as prior knowledge to inject into our PISNet. An iterative reconstruction module is raised to achieve low-rank reorganization of the fused residual features. Meanwhile, a novel loss function is put forward to induce the network training to ensure that each component conform to the prior. The proposed approach innovatively integrates deep learning with semi-parametric methods for RFI suppression, which achieves superior performance on simulated and real data. Compared to existing learning-based methods, the image quality of the restored Sentinel-1 data is improved by 9.37% AG. The code and dataset will be available online (https://github.com/JyuanShen/PISNet).