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Train Localization During GNSS Outages: A Minimalist Approach Using Track Geometry And IMU Sensor Data
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0002-3599-5584
2024 (English)In: FUSION 2024 - 27th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers (IEEE) , 2024Conference paper, Published paper (Refereed)
Abstract [en]

Train localization during Global Navigation Satellite Systems (GNSS) outages presents challenges for ensuring failsafe and accurate positioning in railway networks. This paper proposes a minimalist approach exploiting track geometry and Inertial Measurement Unit (IMU) sensor data. By integrating a discrete track map as a Look-Up Table (LUT) into a Particle Filter (PF) based solution, accurate train positioning is achieved with only an IMU sensor and track map data. The approach is tested on an open railway positioning data set, showing that accurate positioning (absolute errors below 10 m) can be maintained during GNSS outages up to 30 s in the given data. We simulate outages on different track segments and show that accurate positioning is reached during track curves and curvy railway lines. The approach can be used as a redundant complement to established positioning solutions to increase the position estimate's reliability and robustness.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024.
Keywords [en]
discrete track map, particle filter, statistical sensor fusion, train positioning
National Category
Computer graphics and computer vision Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-355922DOI: 10.23919/FUSION59988.2024.10706340ISI: 001334560000068Scopus ID: 2-s2.0-85207695182OAI: oai:DiVA.org:kth-355922DiVA, id: diva2:1911088
Conference
27th International Conference on Information Fusion, FUSION 2024, July 7-11, 2024, Venice, Italy
Note

Part of ISBN 9781737749769, 9798350371420

QC 20250206

Available from: 2024-11-06 Created: 2024-11-06 Last updated: 2025-02-06Bibliographically approved

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Löffler, WendiBengtsson, Mats

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf