Bayesian techniques for onboard train localization

Precise train localization is a prerequisite for any efficient security and disposition tasks in modern transportation systems. In contrast to airplanes or ships, the localization of a train is not satisfyingly solvable with satellite systems. Occlusions in urban areas, tunnels and forests enforce the application of diverse sensor principles or cost intensive installations on track side. This contribution proposes an on-board train localization solely relying on a topological map and an eddy current sensor. The sensor principle is based on electromagnetic induction and is capable of estimating the speed of the train as well as detecting and classifying turnouts. These are represented by nodes in the map and allow absolute localization and recalibration of the distance measurement. Bayesian methods, in particular hidden Markov models in combination with the sequential Monte Carlo method, are used to solve the arising problems of a real-time and generalized detection and classification as well as localization within the topological map.