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

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 safety-critical 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.