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Increasing Accuracy in Train Localization Exploiting Track-Geometry Constraints

Winter, Hanno ; Willert, Volker ; Adamy, Jürgen (2022)
Increasing Accuracy in Train Localization Exploiting Track-Geometry Constraints.
21st International Conference on Intelligent Transportation Systems (ITSC). Maui, HI, USA (04.-07.11.2018)
doi: 10.26083/tuprints-00020410
Conference or Workshop Item, Secondary publication, Postprint

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: Increasing Accuracy in Train Localization Exploiting Track-Geometry Constraints
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2018
Publisher: IEEE
Collation: 8 ungezählte Seiten
Event Title: 21st International Conference on Intelligent Transportation Systems (ITSC)
Event Location: Maui, HI, USA
Event Dates: 04.-07.11.2018
DOI: 10.26083/tuprints-00020410
Corresponding Links:
Origin: Secondary publication service

Train-borne localization systems as a key component of future signalling systems are expected to offer huge economic and operational advances for the railway transportation sector. However, the reliable provision of a track-selective and constantly available location information is still unsolved and prevents the introduction of such systems so far. A contribution to overcome this issue is presented here. We show a recursive multistage filtering approach with an increased cross-track positioning accuracy, which is decisive to ensure track-selectivity. This is achieved by exploiting track-geometry constraints known in advance, as there are strict rules for the construction of railway tracks. Additionally, compact geometric track-maps can be extracted during the filtering process which are beneficial for existing train localization approaches. The filter was derived applying approximate Bayesian inference. The geometry constraints are directly incorporated in the filter design, utilizing an interacting multiple model (IMM) filter and extended Kalman filters (EKF). Throughout simulations the performance of the filter is analyzed and discussed thereafter.

Status: Postprint
URN: urn:nbn:de:tuda-tuprints-204102
Additional Information:

Keywords: Geometry, Rail transportation, Hidden Markov models, Tracking, Bayes methods, Global navigation satellite system, Reliability

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institut für Automatisierungstechnik und Mechatronik > Control Methods and Robotics (from 01.08.2022 renamed Control Methods and Intelligent Systems)
Date Deposited: 01 Feb 2022 13:16
Last Modified: 21 Mar 2023 12:33
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20410
PPN: 491452918
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