Resilient GNSS/INS-Based Railway Train Localization Using Odometer/Trackmap-Enabled Jamming Discrimination

Technological advances in the Global Navigation Satellite System (GNSS) industry have brought significant advantages in enhancing the cost-efficiency of railway applications. However, GNSS vulnerability to external jamming necessitates enhanced protection ability of the GNSS-based train localization system. This paper proposes a resilient train localization solution under the tightly-coupled integration scheme. This solution maximizes the utilization of multi-source information from train-borne sensors, including INS, odometer, and the trackmap database. Based on the existing localization scheme, it achieves a compatible way to address different jamming-intrusion situations without altering the GNSS receiver structure, addressing both the GNSS failure and degradation caused by jamming. Using an odometer/trackmap-enabled equivalent measurement logic, the continuity of localization can be guaranteed against GNSS failure under strong GNSS jamming. A robust filtering algorithm enabled by a jamming discrimination mechanism is proposed for GNSS/INS integration to mitigate the negative effect from degraded GNSS measurements, reducing the hazards by jamming with an intermediate power level. Based on the field data and a jamming test platform, results under two typical jamming scenarios are evaluated to demonstrate the necessity and superiority of the proposed solution. It also emphasizes the importance of the full-life-cycle resilience of GNSS-based train localization under the railway operation environment.

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Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2024-10-09
DOI
https://doi.org/10.1109/tits.2024.3460689
Citations
3
Primary Topic
Vehicle License Plate Recognition
Type
article
Field-Weighted Citation Impact
0.96

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article

Resilient GNSS/INS-Based Railway Train Localization Using Odometer/Trackmap-Enabled Jamming Discrimination

Zhuojian Cao, Wei Jiang, Baigen Cai, Jian Wang et al.
3 citations
IEEE Transactions on Intelligent Transportation Systems
Vehicle License Plate Recognition
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article

Resilient GNSS/INS-Based Railway Train Localization Using Odometer/Trackmap-Enabled Jamming Discrimination

Zhuojian Cao, Wei Jiang, Baigen Cai, Jian Wang, Jiang Liu
article en
3 citations

Abstract

Technological advances in the Global Navigation Satellite System (GNSS) industry have brought significant advantages in enhancing the cost-efficiency of railway applications. However, GNSS vulnerability to external jamming necessitates enhanced protection ability of the GNSS-based train localization system. This paper proposes a resilient train localization solution under the tightly-coupled integration scheme. This solution maximizes the utilization of multi-source information from train-borne sensors, including INS, odometer, and the trackmap database. Based on the existing localization scheme, it achieves a compatible way to address different jamming-intrusion situations without altering the GNSS receiver structure, addressing both the GNSS failure and degradation caused by jamming. Using an odometer/trackmap-enabled equivalent measurement logic, the continuity of localization can be guaranteed against GNSS failure under strong GNSS jamming. A robust filtering algorithm enabled by a jamming discrimination mechanism is proposed for GNSS/INS integration to mitigate the negative effect from degraded GNSS measurements, reducing the hazards by jamming with an intermediate power level. Based on the field data and a jamming test platform, results under two typical jamming scenarios are evaluated to demonstrate the necessity and superiority of the proposed solution. It also emphasizes the importance of the full-life-cycle resilience of GNSS-based train localization under the railway operation environment.

IEEE Transactions on Intelligent Transportation SystemsVol. 25(12)
Beijing Jiaotong University (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 21%
Vehicle License Plate Recognition
0.96
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