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Bayesian Train Localization with Particle Filter, Loosely Coupled GNSS, IMU, and a Track Map

Heirich, Oliver (2016) Bayesian Train Localization with Particle Filter, Loosely Coupled GNSS, IMU, and a Track Map. Journal of Sensors. Hindawi Publishing Corporation. doi: 10.1155/2016/2672640. ISSN 1687-725X.

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Official URL: http://www.hindawi.com/journals/js/2016/2672640/


Train localization is safety-critical and therefore the approach requires a continuous availability and a track-selective accuracy. A probabilistic approach is followed up in order to cope with multiple sensors, measurement errors, imprecise information, and hidden variables as the topological position within the track network. The nonlinear estimation of the train localization posterior is addressed with a novel Rao-Blackwellized particle filter (RBPF) approach. There, embedded Kalman filters estimate certain linear state variables while the particle distribution can cope with the nonlinear cases of parallel tracks and switch scenarios. The train localization algorithmis further based on a trackmap andmeasurements froma GlobalNavigation Satellite System(GNSS) receiver and an inertial measurement unit (IMU). The GNSS integration is loosely coupled and the IMU integration is achieved without the common strapdown approach and suitable for low-cost IMUs.The implementation is evaluated with realmeasurements from a regional train at regular passenger service over 230 km of tracks with 107 split switches and parallel track scenarios of 58.5 km.The approach is analyzed with labeled data by means of ground truth of the traveled switch way. Track selectivity results reach 99.3% over parallel track scenarios and 97.2% of correctly resolved switch ways.

Item URL in elib:https://elib.dlr.de/104268/
Document Type:Article
Title:Bayesian Train Localization with Particle Filter, Loosely Coupled GNSS, IMU, and a Track Map
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Heirich, OliverUNSPECIFIEDhttps://orcid.org/0000-0001-5191-5997
Date:13 June 2016
Journal or Publication Title:Journal of Sensors
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Hindawi Publishing Corporation
Keywords:Railway, train localization, track selective, multi sensor, navigation, Bayesian, particle filter, GNSS, IMU, map
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Terrestrial Vehicles (old)
DLR - Research area:Transport
DLR - Program:V BF - Bodengebundene Fahrzeuge
DLR - Research theme (Project):V - Next Generation Train III (old), V - TrackScan (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Communication and Navigation > Communications Systems
Deposited By: Heirich, Oliver
Deposited On:18 May 2016 18:01
Last Modified:23 Jul 2022 13:44

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