Real-time and precise train position determination is of great significance in train operation control. In recent years, the utilization of satellite navigation technology in railway train positioning has become a new trend. When the train enters or passes through a station with canopy, the observing condition of satellites will be greatly constrained, and the observing quality cannot fulfil the performance requirements of train positioning, i.e. the accuracy, continuity and availability. The ground-based Pseudolite (PL) technology allows us to assist Global Navigation Satellite System (GNSS) under constrained signal observing environments like railway station areas. In this paper, aiming at the optimized design of the spatial distribution that ensures the inherent capability of pseudolites in a station, the architecture of the GNSS/PL-based train positioning system in the station-based operating scenario is introduced. An optimized PL constellation determination method is proposed to cope with the constrained GNSS observing conditions. In this approach, the Performance Indication Factor (PIF) is defined to reflect the capability of a specific pseudolite constellation, and the Firefly Algorithm (FA) is applied to realize the iterative optimization calculation and obtain the optimized pseudolites layout solution. Simulations are performed based on a specific railway station, and the results illustrate the improved capability of the optimized constellation solution, which shows its potential in enhancing the GNSS-based train positioning under different constrained environments.


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    Titel :

    Research on Optimized Pseudolite Constellation Design under Constrained GNSS Environment in Railway Stations


    Beteiligte:
    Zhao, Xiaolin (Autor:in) / Liu, Jiang (Autor:in) / Cai, Baigen (Autor:in) / Lu, Debiao (Autor:in) / Wang, Jian (Autor:in)


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    1152945 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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