AUTHOR=Chen Feiqiang , Liu Zhe , Huang Long , Xie Yuchen , Ren Binbin , Zhou Qin 

TITLE=GNSS interference mitigation method based on deep learning

JOURNAL=Frontiers in Physics

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1535906

DOI=10.3389/fphy.2025.1535906

ISSN=2296-424X

ABSTRACT=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%.