Evaluating Multiple GNSS Data in a Multi-Hypothesis Based Map-Matching Algorithm for Train Positioning

Abstract

For certain types of railroad 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 paper presents a modular multi-hypothesis based map-matching (MHMM) approach providing track-selective localization of the train by fusing the data of a GNSS-receiver with several trainborne positioning sensor data. While previous research mostly focuses on inertial measurement unit (IMU) gyro observations for correctly determining the vehicle’s direction of travel after passing a switch, this contribution analyzes an alternative positioning routine which is based only on the evaluation of additional GNSS measurement data. Considering the GNSS course observation as well as the direction-dependent standard deviation of the GNSS position data, field tests have shown that the MHMM algorithm modified in that way ensures precise and reliable localization of the train in form of fast selecting the correct track after having passed a switch facing.