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
Article (Journal)
Electronic Resource
English
hidden Markov models , SLDS model , false alarm rate , hidden Markov model , expectation‐maximisation algorithm , switching linear dynamic system , train length based detection model , GNSS‐based train integrity monitoring system , train integrity detection method , TIMS , global navigation satellite system , satellite navigation , misdetection rate , Gaussian sum filter , relative distance , EM algorithm , verification procedure , expectation maximisation algorithm , HMM , railway safety , train parting time estimation
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