Railway GNSS Multipath Error Modelling Approach with both Train-Side and Operational Environment Characterization

Ana Kliman, Florian Roessl, Anja Grosch, Omar García Crespillo

Abstract:	Railway transportation systems have high accuracy and high integrity demands for safe localization. In the future, railway signaling is expected to rely on onboard sensors like Global Navigation Satellite Systems (GNSS) in order to reduce installation and maintenance costs. GNSS position determination can be however highly degraded because of the presence of multipath caused by the train roof and the challenging railway environment. In this work, we propose to model multipath error in the pseudorange as a combination of the multipath introduced, on one hand, by the vehicle structure and antenna installation and, on the other hand, by the additional reflections of surrounding buildings and objects. First, we derive a safe multipath error model for the antenna installation on the train roof. Second, we use the obtained error model as a reference model to normalize and characterize multipath in along the railway tracks. The role of different Fault Detection and Exclusion (FDE) algorithms in ensuring model compliance is also evaluated. Finally, the methodology is evaluated and tested with real railway dataset. The characterization of both the permanent train multipath and the operational environment multipath contributes to a safe and robust complete GNSS error description for safety critical navigation in the railway environment.
Published in:	Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024) September 16 - 20, 2024 Hilton Baltimore Inner Harbor Baltimore, Maryland
Pages:	114 - 126
Cite this article:	Kliman, Ana, Roessl, Florian, Grosch, Anja, Crespillo, Omar García, "Railway GNSS Multipath Error Modelling Approach with both Train-Side and Operational Environment Characterization," Proceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024), Baltimore, Maryland, September 2024, pp. 114-126. https://doi.org/10.33012/2024.19696
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