INS-aided GNSS jamming protection in support of resilient train positioning

Zhuojian Cao , Jiang Liu , Wei Jiang , Baigen Cai

High-speed Railway ›› 2025, Vol. 3 ›› Issue (3) : 185 -193.

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High-speed Railway ›› 2025, Vol. 3 ›› Issue (3) : 185 -193. DOI: 10.1016/j.hspr.2025.05.004
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INS-aided GNSS jamming protection in support of resilient train positioning

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Abstract

Railway safety and efficiency increasingly rely on precise train positioning. The integration of the Global Navigation Satellite System (GNSS) into railway control systems aims to reduce dependence on track-side infrastructure. While GNSS has significantly improved train localization, challenges such as the susceptibility to jamming remain. To address this, this paper introduces an Inertial Navigation System (INS)-aided train positioning system based on deep integration, exploring its performance through semi-physical experiments and simulations. Experimental results demonstrate that the proposed solution is able to reduce the positioning error by 63.47 %, and the velocity error by 58.47 % under jamming conditions. The study highlights the potential of deep integration for improving the resilience of GNSS-based train control systems, especially in the face of Radio Frequency (RF) jamming threats.

Keywords

GNSS jamming / Deeply-coupled integration / GNSS/INS

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Zhuojian Cao, Jiang Liu, Wei Jiang, Baigen Cai. INS-aided GNSS jamming protection in support of resilient train positioning. High-speed Railway, 2025, 3(3): 185-193 DOI:10.1016/j.hspr.2025.05.004

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CRediT authorship contribution statement

Baigen Cai: Funding acquisition. Zhuojian Cao: Formal analysis. Jiang Liu: Writing – review & editing, Project administration, Investigation, Funding acquisition. Wei Jiang: Supervision, Investigation, Funding acquisition.

Declaration of Competing Interest

Baigen Cai is an editorial board member for High-speed Railway and was not involved in the editorial review or the decision to publish this article. All authors declare that there are no competing interests.

Acknowledgement

This research was supported by the Beijing Natural Science Foundation (4232031), and the National Natural Science Foundation of China (T2222015, U2268206).

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