Prior Knowledge Constraints Network (PKCNet) for Synthetic Aperture Radar Pulse Radio Frequency Interference Suppression

Pulse radio frequency interference (PRFI), which has the potential to corrupt data and lower the quality of synthetic aperture radar (SAR) images, poses a serious threat to the integrity of SAR data. Traditional algorithms often introduce varying degrees of corruption into uncontaminated data and overlook the unique characteristics of SAR signals. This letter proposes a deep neural network with prior knowledge constraints (PKCNet) in the time-frequency domain to address the above issue, specifically designed to suppress PRFI while preserving uncontaminated data. This algorithm incorporates a regional constraint on the time-frequency spectrogram to safeguard uncontaminated data and designs a low-rank reconstruction module (LRM) based on the low-rank characteristic of SAR signals and the sparse characteristic of PRFI. A novel loss function is introduced to guide network training. Compared with frequency-domain notch filtering, time-frequency domain notch filtering, robust principal component analysis, and existing deep-learning-based algorithms, the images of Sentinel-1 recovered by PKCNet exhibit improvements of 19.20%, 2.68%, 7.97%, and 2.05% in terms of the energy of gradient (EOG). The code and dataset will be accessible online (https://github.com/ZhengFenghao/PKCNet).