GNSS (Global Navigation Satellite System) is virtually becoming an autonomous train localization technology for the next-generation train control system. However, potential threats from the intentional interference may severely degrade the availability of GNSS due to its vulnerability. It is of great significance to detect and isolate the negative effects from GNSS interference for the Train Control System (TCS) in the railway field. For the protection against GNSS jamming, extra information from the Inertial Navigation System (INS) and odometer are involved, and an INS/odometer/trackmap-aided GNSS localization method for railway trains is raised in this paper. While the GNSS receiver cannot identify the real signals under a high-power jamming attack condition, a prediction deduced train position generation approach is proposed. In this strategy, velocity from the odometer and the geospatial constraint from the trackmap are involved to calibrate INS, with which continuous positioning is realized under a GNSS-denied situation. Furthermore, while the measurements degradation occurs caused by a relatively low power jamming, a residual-test-based detection solution based on the deviation between the predicted reference pseudo-ranges and the real ones is proposed to isolate degraded measurements. Results from an experiment under a GPS jamming condition demonstrate that the proposed solution outperforms the GPS Single Point Positioning (SPP) and the conventional GPS/INS method. The jamming protection and continuous positioning performance under specific jamming conditions enhance the capability of resilient train positioning.


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    Title :

    INS/Odometer/Trackmap-aided Railway Train Localization under GNSS Jamming Conditions


    Contributors:
    Cao, Zhuojian (author) / Liu, Jiang (author) / Jiang, Wei (author) / Cai, Baigen (author) / Wang, Jian (author)


    Publication date :

    2022-06-05


    Size :

    819376 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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