An Unscented Kalman Filter-based Synchronization Control Approach for Communication-Based Train Control Systems

Ismail Faruqi, M. Brahma Waluya, Yul Yunazwin Nazaruddin, Tua Agustinus Tamba, Augie Widyotriatmo


Communication-based train control (CBTC) system is an advanced train signalling and control technology which is developed using the moving block signalling (MBS) framework. The CBTC system has been shown to be capable of improving the operational efficiency, line capacity and safety of the railway operation. The main objective in implementing the MBS framework in CBTC system is to minimize the train headways through the utilization of an inter-train continuous communication system that determine and control the position of each train more precisely. One important challenge in such an implementation is the fulfillment of the necessary requirement of having highly accurate train localization method to ensure the safety of the short headway operation. This paper describes the results from experimental examination and application of a synchronization control strategy for the CBTC system using an unscented Kalman filter (UKF)-based sensor fusion approach as the localization method. In the proposed approach, the train localization task is performed using an UKF-based sensor fusion method which fuses measurement data from speed sensors and radio frequency identification tags. A synchronization control approach to ensure the safety movement of the train convoy in curved railway tracks under the MBS scheme is then proposed. The results presented in this paper show that the proposed localization and synchronization control methods can significantly improve the localization accuracy and reduce the inter-train headways.


CBTC; moving block signaling; synchronization control; unscented Kalman filter.

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PT. Kereta Commuter Indonesia. (2019) Annual Report 2017. [Online]. Available: (retrieved on 10 April 2019).

M.J. Lockyear,“Changing track: moving-block railway signaling,” IEE Review, vol. 42, no. 1, pp. 21-25, 1996.

C. Schifers and G. Hans, “IEEE standard for communications-based train control (CBTC) performance and functional requirements,” in Proc. Vehicular Technology Conf., pp. 1581-1585, 2000.

W. C. Carreño, “Efficient driving of CBTC ATO operated trains,” Ph.D. thesis, KTH Royal Institute of Technology, Stockholm Sweden, 2017.

S. Thurn, W. Burgard, and D. Fox, Probabilistic Robotics, Boston, MA, USA: MIT Press, 2006.

A. Mirabadi, N. Mort, and F. Schmid, “Application of sensor fusion to railway systems,” in Proc. IEEE MFI, pp. 185-192, 1996.

D. Veillard, F. Mailly, and P. Fraisse, “EKF-based state estimation for train localization,” in Proc. IEEE Sensors, pp. 1-3, 2016.

D. Lu and E. Schnieder, “Performance evaluation of GNSS for train localization,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 2, pp. 1054-1059, 2015.

J. Marais, J. Beugin, and M. Berbineau, “A survey of GNSS-based research & developments for the European railway signaling,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 10, pp. 2602-2618, 2017.

J. Otegui, A. Bahillo, I. Lopetegi, and L. E. Díez, “Evaluation of experimental GNSS and 10-DOF MEMS IMU measurements for train positioning,” IEEE Trans. Instrum. Meas., vol. 68, no. 1, pp. 269-279, 2019.

K. Kim, S. Seol, and S. Kong, “High-speed train navigation system based on multi-sensor data fusion and map matching algorithm,” Int. J. Control Autom. Syst., vol. 13, no. 3, pp. 503-512, 2015.

G. Muniandi and E. Deenadayalan, “Train distance and speed estimation using multi sensor data fusion,” IET Radar, Sonar, Nav., vol. 13, no. 4, pp. 664-671, 2019.

F. Tschopp et al., “Experimental comparison of visual-aided odometry methods for rail vehicles,” IEEE Robot. Autom. Lett., vol. 4, no. 2, pp. 1815-1822, 2019.

M. Lauer and D. Stein, “A train localization algorithm for train protection systems of the future,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 2, pp. 970-979, 2015.

O. Heirich, “Bayesian train localization with particle filter, loosely coupled GNSS, IMU, and a track map," J. Sensors, Art. 2672640, 2016.

J. Otegui, A. Bahillo, I. Lopetegi, and L. E. Díez, “A survey of train positioning solutions,” IEEE Sens. J., vol. 17, no. 20, pp. 6788-6797, 2017.

E. A. Wan and R. V. D. Merwe, "The unscented Kalman filter for nonlinear estimation,” in Proc. IEEE ASSPCC, pp. 153-158, 2000.

S. J. Julier and J. K. Uhlmann, "A new extension of the Kalman filter to nonlinear system,” in Proc. SPIE SPSFTR Conf., pp. 182-194, 1997.

S. J. Julier and J. K. Uhlmann, "Unscented filtering and nonlinear estimation,"Proc. IEEE, vol. 92, no. 3, pp. 401-422, 2004.

S. J. Julier, “The scaled unscented transformation,” in Proc. American Control Conf., pp. 4555-4559, 2002.

I. Faruqi et al., “Train localization using unscented Kalman filter–based sensor fusion,” Int. J. Sust. Transp. Technol., vol. 1, no. 2, pp. 35-41, 2018.

Y. Y. Nazaruddin et al., “On using unscented Kalman filter based multi sensors fusion for train localization,” in Proc. ASCC, 2019.

R. Takagi, “Synchronization control of trains on the railway track controlled by the moving block signaling system,” IET Electric. Syst. Transp., vol. 2, no. 3, pp. 130-138, 2012.

C. Bersani et al., “Rapid, robust, distributed evaluation and control of train scheduling on a single line track,” Control Eng. Pract., vol. 35, pp. 12-21, 2015.



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