Using Probabilistic Geometrical Map Information For Train Localization

Determination of train positions within a railway network must be fail-safe and of high accuracy. This is an essential task to solve to achieve a secure and efficient railway operation. In this paper, we present a method to estimate position and velocity of a train in the track net using given position estimates from an arbitrary information source, and improving the estimate by using geometrical track information. We focus on modelling and exploiting of the geometrical track information including possible uncertainties and examine the impact of uncertainties on the state estimate. We store the track information as a set of supporting points with Gaussian uncertainties and interpolate linearly. The track information is fed into a Kalman filter in form of soft constraints that is modified to account for state-dependent observation noise. A simulated test run shows that the average position and velocity error along track decreases significantly when modelling the uncertainty of the constraints, compared to using a Kalman filter with hard constraints. We evaluate the presented filter for different supporting point and measurement uncertainties and show that the performance within a typical parameter setting for train positioning is improved compared to the unconstrained Kalman filter and the Kalman filter with hard constraints.