Train Localization During GNSS Outages: A Minimalist Approach Using Track Geometry And IMU Sensor Data

TL;DR

This paper introduces a minimalist train localization method during GNSS outages using track geometry and IMU data, achieving sub-10 meter accuracy with a particle filter and track map integration.

Contribution

It presents a novel, lightweight approach combining track geometry and IMU data within a particle filter framework for train localization during GNSS outages.

Findings

Achieves absolute positioning errors below 10 meters during outages

Maintains accuracy for up to 30 seconds of GNSS loss

Effective on curved and complex railway segments

Abstract

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.