GNSS interference mitigation method based on deep learning - Chen, Feiqiang; Liu, Zhe; Huang, Long; Xie, Yuchen; Ren, Binbin; Zhou, Qin (Frontiers in Physics, 2025)
The interference environment faced by GNSS receivers is unknown, dynamic, and uncertain, making it difficult for a single interference mitigation method to address all interference threats. In this paper, we introduce an intelligent interference mitigation approach. By leveraging a deep learning network model, our method automatically selects the optimal interference mitigation technique based on the specific characteristics of the interference. This enhances the receiver’s anti-jamming performance and overall robustness. Our experimental results show that the proposed method effectively suppresses narrowband interference, pulse interference, and chirp interference, demonstrating insensitivity to interference parameters. Statistically, it outperforms traditional methods, with the proportion of the carrier-to-noise ratio (C/N0) above a given threshold (initial C/N0 reduced by 3 dB) increasing by over 10%.