Keywords: train detection, cross-correlation, Fiber-Bragg grating sensor, speed detection, vehicle modelling, axle counter.

Train detection is a critical research topic in the railway field, that influences the modern safety signalling systems. The solutions commonly used (i.e. track circuits and axle counters) are affected by low reliability and a high cost of implementation. This paper presents a train detection algorithm, able to estimate the train speed, the crossing times on a fixed point of the track and the axle number, starting from the knowledge of the vertical forces on the sleepers. The algorithm makes use of cross-correlation techniques, characterized by a high robustness against input noise and disturbances. A suitable and accurate model of railway vehicle and flexible track has been also developed to test the algorithm under any operating conditions. The railway vehicle chosen as benchmark is the Manchester Wagon. Different simulation studies have been made to test the performance and the robustness of the proposed solution and the results are quite encouraging.

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