Robust Particle Filter for Magnetic field-based Train Localization

For the automation of railway systems, a crucial requirement is a reliable localization system that is able to localize all trains in the track network. While in most parts of the track network, GNSS signals are available and provide a satisfying navigation solution, there are also parts where shadowing and multipath renders GNSS signals unavailable. In such scenarios, magnetic field-based localization can complement GNSS. Magnetic field localization is based on the observation that ferromagnetic material in the vicinity of a railway track introduces distortions in the Earth magnetic field. These distortions are persistent over time and therefore can be used for localization when stored in a map. In our prior work we showed that particle filters can be used to perform magnetic localization. In this paper, we extend the particle filter with fault detection and exclusion capabilities to make it more robust and accurate in the presence of sensor faults and measurement errors. The advantage of the proposed particle filter w.r.t. accuracy and robustness is shown in an evaluation with measurement data.