On the Integrity of GNSS-IMU Train Positioning Exploiting the Track Constraint

Alessandro Neri, Michele Brizzi, Alessia Vennarini, and Francesco Rispoli

Peer Reviewed

Abstract:	In this study, we introduce a novel approach for enhancing the accuracy and reliability of positioning systems in railway applications. Our method focuses on utilizing the inherent constraints of railway tracks to improve positioning in areas where Global Navigation Satellite System (GNSS) signals are unavailable, as well as in areas where GNSS signals are accessible. The core of our approach involves the combined use of an Inertial Measurement Unit (IMU) and a digital map of the railway. More specifically, we exploit the redundancy of the accelerometers and gyros measurements, implied by the track constraint, in order to design a fault detection and exclusion algorithm operating in the parity space, and an integrity monitoring algorithm that will provide the protection levels for the estimated train position and speed. In addition, when the digital map indicates that the train is operating on a straight track segment only GNSS observations and longitudinal acceleration are employed to determine position, speed and related confidence intervals. The normal and lateral acceleration measurements and the gyro angular rates, whose value in theory is null, are employed to calibrate the IMU and to detect faulty components. Full IMU measurements are employed in the proximity and along curved tracks as well as in the proximity of junctions and crossings to verify the proper functioning of the rail signalling system.
Published in:	Proceedings of the ION 2024 Pacific PNT Meeting April 15 - 18, 2024 Hilton Waikiki Beach Honolulu, Hawaii
Pages:	463 - 476
Cite this article:	Neri, Alessandro, Brizzi, Michele, Vennarini, Alessia, Rispoli, Francesco, "On the Integrity of GNSS-IMU Train Positioning Exploiting the Track Constraint," Proceedings of the ION 2024 Pacific PNT Meeting, Honolulu, Hawaii, April 2024, pp. 463-476. https://doi.org/10.33012/2024.19659
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