Map-Supported Positioning Enables In-Service Condition Monitoring of Railway Tracks

Abstract

We demonstrate how positioning concepts enable in-service condition monitoring of railway tracks. Specifically, it is shown that accurate georeferencing of monitoring data can be achieved by sensor fusion of GNSS and IMU measurements with a map of the railway network. Because such georeferencing is an offline positioning problem, a two-stage approach that operates on batches of data is developed: First, path hypotheses are estimated from the GNSS data and the railway map. Second, a nonlinear Rauch-Tung-Striebel smoother provides on-track positions and speeds in path coordinates given each path hypothesis and the IMU and GNSS data. The developed methods are an essential part of a track condition monitoring system developed at DLR. The positioning results are used for the track-dependent analysis of axle-box-acceleration data. Accordingly, all results shown in this paper have been obtained on real data collected in the harbor railway network of Braunschweig, Germany.