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

Ismail Faruqi (1), M. Brahma Waluya (2), Yul Yunazwin Nazaruddin (3), Tua Agustinus Tamba (4), Augie Widyotriatmo (5)
(1) Instrumentation and Control Research Group, Institut Teknologi Bandung, Bandung 40142, Indonesia
(2) Instrumentation and Control Research Group, Institut Teknologi Bandung, Bandung 40142, Indonesia
(3) Instrumentation and Control Research Group, Institut Teknologi Bandung, Bandung 40142, Indonesia
(4) Department of Electrical Engineering, Parahyangan Catholic University, Bandung 40141, Indonesia
(5) Instrumentation and Control Research Group, Institut Teknologi Bandung, Bandung 40142, Indonesia
Fulltext View | Download
How to cite (IJASEIT) :
Faruqi, Ismail, et al. “An Unscented Kalman Filter-Based Synchronization Control Approach for Communication-Based Train Control Systems”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 6, Dec. 2019, pp. 1849-55, doi:10.18517/ijaseit.9.6.10243.
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.

PT. Kereta Commuter Indonesia. (2019) Annual Report 2017. [Online]. Available: http://www.krl.co.id (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.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).