A Novel Approach for Reducing Train Localization Errors by Inertial Measurements

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Marinoni, Mauro, Amato, Pierluigi, Di Franco, Carmelo, Sabina, Salvatore, Buttazzo, Giorgio
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Curvature reconstruction; Digital mapping; Errors; Feature extraction; Global navigation satellite system; Inertial navigation; Inertial sensing devices; Localization; Location awareness; Map matching; Odometers; Odometry; Rail transportation; Railroad signaling equipment; Railway odometry; Railway tracks; Railways; Sensors; Signalling systems; Switches; System effectiveness; Train localisation; Trains
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3236784

The effectiveness of current railway signaling systems heavily depends on the accuracy in the localization of trains on the track, which is currently enforced using static markers mounted along the line, called balises. However, balises are expensive, sparse, prone to tampering, and subject to maintenance, hence they need to be integrated by additional onboard sensors. Also, the harsh operational environment and the strict safety regulation do not allow a straightforward adoption of solutions from other application domains, like the GNSS in automotive systems. Hence, finding novel cost-effective solutions to improve the localization accuracy is an emerging research topic in modern railway systems. Low-cost inertial sensors provide the required level of availability and thus represent a viable approach to integrate physical balises, but they need to be coupled with a rigorous methodology for integrating the inertial data with the track geometry features extracted from a digital railway map. This paper presents a novel methodology for exploiting inertial data in train localization. It is based on a two-phase approach: in an offline phase, track data from the digital map are analyzed to extract a number of features (curves, switches, slopes, etc.) that are stored in the map as position markers; at run time, such markers are detected by analyzing the inertial data and matched with those stored in the map. Such a marker matching allows reducing odometry errors by a significant extent. Preliminary experiments on synthetic and real data show the effectiveness of the proposed approach in enhancing train odometry.