HMM Based Segmentation of Continuous Eddy Current Sensor Signals

Abstract

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.