Using Probabilistic Geometrical Map Information For Train Localization
- Resource Type
- Conference
- Authors
- Loffler, Wendi; Bengtsson, Mats
- Source
- 2022 25th International Conference on Information Fusion (FUSION) Information Fusion (FUSION), 2022 25th International Conference on. :01-08 Jul, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Location awareness
Uncertainty
Measurement uncertainty
Information filters
Probabilistic logic
Rail transportation
Kalman filters
Kalman filter
probabilistic track map
stochastic modelling
train positioning
- Language
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.