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ORIGINAL RESEARCH article
Front. Phys.
Sec. Interdisciplinary Physics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1535906
GNSS Interference Mitigation Method Based on Deep Learning
Provisionally accepted- National University of Defense Technology, Changsha, China
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%.
Keywords: Satellite navigation, interference mitigation, deep learning, Receiver, Interfering signals
Received: 29 Nov 2024; Accepted: 19 Feb 2025.
Copyright: © 2025 Chen, Liu, Huang, Xie, Ren and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhe Liu, National University of Defense Technology, Changsha, China
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