Train integrity whilst in service establishes the foundation for railway safety. This study investigates train integrity detection which reliably deduces whether the train consists remain intact. A switching linear dynamic system (SLDS) based train integrity detection method is proposed for Global Navigation Satellite System (GNSS) based train integrity Monitoring System (TIMS) using the relative distance, velocity and acceleration of the locomotive and the last van. There, Expectation Maximisation (EM) algorithm estimates the parameters of SLDS model while the Gaussian Sum Filter infers train integrity state. After that, to cope with false detection and misdetection, a verification procedure and train parting time estimation are designed. The approach is evaluated with both field trials and simulated data. Results show that the false alarm rate and misdetection rate of SLDS-based integrity detection approach are 0 and 0.09% respectively, which proves better than the estimated train length based detection model and Hidden Markov Model (HMM).
Switching LDS detection for GNSS-based train integrity monitoring system
IET Intelligent Transport Systems ; 11 , 5 ; 299-307
2017-06-01
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
expectation maximisation algorithm , verification procedure , global navigation satellite system , expectation-maximisation algorithm , HMM , satellite navigation , SLDS model , relative distance , hidden Markov model , hidden Markov models , train length based detection model , EM algorithm , train integrity detection method , TIMS , railway safety , train parting time estimation , GNSS-based train integrity monitoring system , false alarm rate , switching linear dynamic system , Gaussian sum filter , misdetection rate
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