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Train Localization During GNSS Outages: A Minimalist Approach Using Track Geometry And IMU Sensor Data
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Teknisk informationsvetenskap.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Teknisk informationsvetenskap.ORCID-id: 0000-0002-3599-5584
2024 (Engelska)Ingår i: FUSION 2024 - 27th International Conference on Information Fusion, Institute of Electrical and Electronics Engineers (IEEE) , 2024Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE) , 2024.
Nyckelord [en]
discrete track map, particle filter, statistical sensor fusion, train positioning
Nationell ämneskategori
Datorgrafik och datorseende Signalbehandling
Identifikatorer
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
Konferens
27th International Conference on Information Fusion, FUSION 2024, July 7-11, 2024, Venice, Italy
Anmärkning

Part of ISBN 9781737749769, 9798350371420

QC 20250206

Tillgänglig från: 2024-11-06 Skapad: 2024-11-06 Senast uppdaterad: 2025-02-06Bibliografiskt granskad

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Löffler, WendiBengtsson, Mats

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Totalt: 81 träffar
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