Multi-Hypothesis Based Map-Matching Algorithm for Precise Train Positioning

Abstract

For certain types of railway lines replacing the equipment for precise train positioning along the track by suitable low-cost sensors and a digital map on the train can result in a more cost-efficient railway operation. This contribution presents a multi-hypothesis based map-matching approach to fuse the data of a GPS receiver, a radar and an inertial measurement unit (IMU). Taking into account only GPS and radar data does not provide a track-selective positioning information on parallel lines, which is essential for safetycritical applications. However, the result of this contribution shows that analyzing additionally the curvature data obtained from an inertial measurement unit while the train passes a switch and comparing it to a reference pattern gained from the design parameters of the switch enables a track-selective positioning.