access icon free Adaptive multisensor data fusion technique for train localisation and detection of accidental train parting

© The Institution of Engineering and Technology
Received 17/11/2017, Accepted 10/04/2018, Revised 06/04/2018, Published 12/04/2018

This study proposes a bilinear recursive least square based adaptive multisensor data fusion technique for the precise localisation of railway vehicles and detection of an accidental train parting to be used with the train collision avoidance system (TCAS) in Indian railways. The accurate localisation of railway vehicles during the absence of global positioning system (GPS) is a challenging task for the TCAS. One of the reliable solutions for this task may be the augmentation of GPS with the onboard multisensor system. A bilinear recursive least square adaptive filter is used here to estimate and compensate the position error of the onboard multisensor system. The impact of slack in coupling is considered for the analysis of parting detection. The performance of the proposed technique is compared with the observation error based approach, bounded offset based approach and pseudo-measurement state constraining technique. The simulation results indicate that the proposed technique is superior in terms of positional accuracy and for the detection of an accidental train parting with a minimum parting distance.

Inspec keywords: sensor fusion; least squares approximations; railways; adaptive filters

Other keywords: bilinear recursive least square adaptive filter; TCAS; train detection; train localisation; Indian railways; bilinear recursive least square based adaptive multisensor data fusion technique; train collision avoidance system

Subjects: Interpolation and function approximation (numerical analysis); Filtering methods in signal processing; Computing in other engineering fields; Interpolation and function approximation (numerical analysis); Sensor fusion

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