Track Occupation Detection Based on a Maximum Posterior Probability Model Using Multisensor Data Fusion

To address the railway track section occupation detection failure, this paper proposes a maximum posterior probability model that uses multisensor information fusion to detect track occupation. Based on the installation method of the sensor, this model obtains stable base data of occupied track sections and extracts its features, including train's running velocity, acceleration, direction, occupied area, wheelset axle counting, and track vibration. The maximum posterior probability and logarithm model are then derived by computing the prior probability, the posterior probability, and the conditional joint probability for the features. The judgment of the track occupation is more accurate compared with experience value. The experiments demonstrate that our track occupation detection method is able to effectively judge the occupation of the train, persons, and tool cart. Based on the maximum posterior probability, the Bayes optimal data fusion ratio for a measured parameter in this paper reaches 99.9983%.