GNSS plays a strategic role on the introduction of the Virtual Balise functionality and the train integrity. Thanks to GNSS, it could be possible to realize cost-effective solutions to increase the safety in the regional lines, where the traffic density is lower. The train position estimation is implemented taking into account that the train is constrained to lie on the track (i.e. track constraint). In this way, we can express the position in terms of the curvilinear abscissa (progressive mileage) of the track corresponding to the train position. However, the impact of local effects such as multipath, foliage attenuation and shadowing in the railway environment plays a crucial role due to the presence of infrastructures like platform roofs, side walls, tunnel entrances, buildings and so on close to the trackside. In the paper, we analyse the impact of those threats on the train GNSS-based position estimation performance. At this aim, several scenarios have been generated by using both real data acquired on a railway test-bed in Sardinia, and synthetic data generated in the lab through ad hoc multipath and foliage models. A sensitivity analysis has been conducted, varying main scenarios parameters (e.g. height of obstacles, presence of trees and shadowing). The result of the performed analysis, in terms of availability, accuracy and integrity, are here presented. mitigations implemented by the ERTMS at system level are not considered since the attention is focused on GNSS only.

Stallo, C., Neri, A., Salvatori, P., Coluccia, A., Capua, R., Olivieri, G., et al. (2018). GNSS-based location determination system architecture for railway performance assessment in presence of local effects. In 2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings (pp.374-381). Institute of Electrical and Electronics Engineers Inc. [10.1109/PLANS.2018.8373403].

GNSS-based location determination system architecture for railway performance assessment in presence of local effects

Neri, A.
;
Salvatori, P.;
2018-01-01

Abstract

GNSS plays a strategic role on the introduction of the Virtual Balise functionality and the train integrity. Thanks to GNSS, it could be possible to realize cost-effective solutions to increase the safety in the regional lines, where the traffic density is lower. The train position estimation is implemented taking into account that the train is constrained to lie on the track (i.e. track constraint). In this way, we can express the position in terms of the curvilinear abscissa (progressive mileage) of the track corresponding to the train position. However, the impact of local effects such as multipath, foliage attenuation and shadowing in the railway environment plays a crucial role due to the presence of infrastructures like platform roofs, side walls, tunnel entrances, buildings and so on close to the trackside. In the paper, we analyse the impact of those threats on the train GNSS-based position estimation performance. At this aim, several scenarios have been generated by using both real data acquired on a railway test-bed in Sardinia, and synthetic data generated in the lab through ad hoc multipath and foliage models. A sensitivity analysis has been conducted, varying main scenarios parameters (e.g. height of obstacles, presence of trees and shadowing). The result of the performed analysis, in terms of availability, accuracy and integrity, are here presented. mitigations implemented by the ERTMS at system level are not considered since the attention is focused on GNSS only.
2018
9781538616475
Stallo, C., Neri, A., Salvatori, P., Coluccia, A., Capua, R., Olivieri, G., et al. (2018). GNSS-based location determination system architecture for railway performance assessment in presence of local effects. In 2018 IEEE/ION Position, Location and Navigation Symposium, PLANS 2018 - Proceedings (pp.374-381). Institute of Electrical and Electronics Engineers Inc. [10.1109/PLANS.2018.8373403].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/340378
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