An exact localization of trains is essential for effective disposition and design of modern train operating systems, allowing a better use of the given infrastructure. In this paper we propose to use turnouts on rail tracks as absolute landmarks and re-calibration points for onboard location systems. The measurements base on an eddy current sensor system, additionally providing speed information through correlating inhomogeneities along the rail track. This paper presents a hidden Markov model approach that offers a robust detection and separation of turnouts. The proposed algorithm makes it possible to process whole train stations with successive turnouts continuously, to perform a first low-level classification and to separate close events that can be accurately cut out of the signal, which is a basis for an advanced classification.


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    Title :

    HMM Based Segmentation of Continuous Eddy Current Sensor Signals


    Contributors:


    Publication date :

    2008-10-01


    Size :

    582843 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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