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Precise localization of rail vehicles is a key element toward the development and deployment of novel train control systems that offer enhanced security and efficiency. Typically, research on train navigation systems approaches this task either by data fusion of an increasing number of onboard sensors or by additional infrastructure installations that are combined with the localization of a global navigation satellite system (GNSS). The former approach is cost intensive and only gradually improves reliability and availability of localization information, whereas the latter approach suffers from the absence of satellite signals in places that are important for railroad applications such as tunnels or railway stations. In contrast, this paper employs a novel single eddy current sensor (ECS) mounted on the rail vehicle that directly pursues observations of the rail on a topological map. The localization task is formulated in a model-based probabilistic framework that enables us to derive signal processing techniques for board-autonomous speed estimation and recognition of particular events such as railroad switches by pattern recognition. In particular, turnouts are detected by Bayesian inference based on hidden Markov models (HMMs). In the final step, position on a topological map is estimated by sequential Monte Carlo sampling that combines speed and event information acquired from the ECS signal. Experiments with simulated and real-world data from an experimental rail vehicle indicate that the proposed system yields position and speed information of high reliability in real time.