Bayesian Train Localization Method Extended By 3D Geometric Railway Track Observations From Inertial Sensors

Abstract

Train localization is safety critical and requires a highly robust, accurate and track selective localization. The proposed approach focuses on an on-board localization system using appropriate onboard sensors without any additional railway infrastructure. Satellite navigation systems (GNSS) are very beneficial for this task, but the accuracy, measurement errors and the lack of availability in parts of the railway environment do not fulfill the requirements for a safety-of-life railway system. An inertial measurement unit (IMU) is used to increase robustness and accuracy, by sensing the position related effects of 3D track geometry on the train. In order to cope with multiple sensors, position related uncertainties and sensor errors, we propose probabilistic approach with a Bayesian filter. In this paper we present a train localization approach using a particle filter fusing multiple on-board train sensors measurements with a known track map. The particle filter estimates a topological position directly in the track map.