SAR Radio Frequency Interference Suppression Based on Kurtosis-Guided Attention Network

Radio-frequency interference (RFI) severely degrades the imaging quality of synthetic aperture radar (SAR), especially when the interference energy is strongly coupled with ground backscatter in both the time and frequency domains. Existing algorithms typically rely on energy contrast or component decomposition in transform domains, which limits their ability to cleanly separate complex RFI from high-power echoes. Exploiting the fact that kurtosis is insensitive to ground clutter and background noise, this paper proposes an interference suppression network based on the temporal kurtosis guidance mechanism. Specifically, a statistical prior vector capturing the non-Gaussian characteristics of RFI is constructed using kurtosis in the time–frequency domain and is integrated into a multi-scale attention mechanism, allowing the network to more effectively concentrate on interfered regions. Meanwhile, a systematic framework is established for the quantitative assessment of phase fidelity in the reconstruction of complex-valued SAR echoes. On this basis, by exploiting the strong generalization capability and high processing efficiency of data-driven models, the proposed network achieves improved RFI separation and enhanced reconstruction accuracy of underlying scene features. Ablation experiments validated that the design of a kurtosis-guided module can reduce the mean square error (MSE) loss by 14.87% compared to the basic model. Furthermore, regarding the phase fidelity, the correlation coefficient between the suppressed signal and the original true signal reached 0.99. Finally, GF-3 satellite data are used to further demonstrate the effectiveness and practicality of the proposed method.