Abstract
This paper investigates the problem of track occupancy detection in distributed settings. Track occupancy detection determines which tracks are occupied in a railway system. For each track, the Neyman–Pearson structure is applied to reach the local decision. Globally, it is a multiple hypotheses testing problem. The Bayesian approach is employed to minimize the probability of the global decision error. Based on the prior probabilities of multiple hypotheses and the approximation of the receiving operation characteristic curve of the local detector, a person-by-person optimization method is implemented to obtain the fusion rule and the local strategies off line. The results are illustrated through an example constructed from in situ devices.
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