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Train localization using an adaptive multisensor data fusion technique

    Bidhan Malakar Affiliation
    ; Binoy Krishna Roy Affiliation

Abstract

This work deals with the development of an adaptive multisensor data fusion technique for the accurate estimation of the trains position and velocity. The proposed technique will work with the Train Collision Avoidance System (TCAS) used in Indian railways during Global Positioning System (GPS) outages. The determination of accurate position of trains is a challenging task for the TCAS during GPS outages. The accuracy of the proposed Volterra Recursive Least Square (VRLS) based adaptive multisensor data fusion technique is evaluated by generating two kinematic profiles for a passenger train running between Silchar–Lumding broad gauge route in Indian railways. The effect of accelerometer bias is also considered during the analysis. It is observed that the developed technique can provide a better estimate of the position and velocity for the TCAS especially during GPS outages and without using any additional railway infrastructure. The simulation results indicate that the proposed technique is superior to the earlier works in terms of achieving better positional accuracy in presence of accelerometer bias.

Keyword : train positioning, multisensor data fusion, signal processing, train collision avoidance system, global positioning system, odometer, accelerometer

How to Cite
Malakar, B., & Roy, B. K. (2019). Train localization using an adaptive multisensor data fusion technique. Transport, 34(4), 508-516. https://doi.org/10.3846/transport.2019.11313
Published in Issue
Oct 16, 2019
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Acharya, A.; Sadhu, S.; Ghoshal, T. K. 2011. Train localization and parting detection using data fusion, Transportation Research Part C 19(1): 75–84. https://doi.org/10.1016/j.trc.2010.03.010

Arunachalam, S. S.; Guruprasad, M.; Koti, B. 2013. Performance of radar assisted satellite based automated train transit system, 2013 International Conference on Communication and Signal Processing, 3–5 April 2013, Melmaruvathur, India, 758–762. https://doi.org/10.1109/iccsp.2013.6577158

Bachmann, C.; Abdulhai, B.; Roorda, M. J.; Moshiri, B. 2012. Multisensor data integration and fusion in traffic operations and management, Transportation Research Record: Journal of the Transportation Research Board 2308: 27–36. https://doi.org/10.3141/2308-04

Bajaj, R.; Ranaweera, S. L.; Agrawal, D. P. 2002. GPS: location-tracking technology, Computer 35(4): 92–94. https://doi.org/10.1109/MC.2002.993780

Bar-Shalom, Y.; Li, X.-R. 1993. Estimation and Tracking: Principles, Techniques, and Software, Artech House. 511 p.

Chang, C. S.; Tsang, K. F. 2008. A cost effective approach on railway vehicle identification and positioning using RFID technology, IET 2nd International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2008), 12–15 October 2008, Beijing, China, 490–493. https://doi.org/10.1049/cp:20081043

Chen, Y.; Han, C.-Z. 2005. Maneuvering vehicle tracking based on multi-sensor fusion, Acta Automatica Sinica 31(4): 625–630.

Cooper, S.; Durrant-Whyte, H. 1994. A Kalman filter model for GPS navigation of land vehicles, in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’94), 12–16 September 1994, Munich, Germany, 1: 157–163. https://doi.org/10.1109/IROS.1994.407396

Diniz, P. S. R. 2013. Adaptive Filtering: Algorithms and Practical Implementation. Springer. 652 p. https://doi.org/10.1007/978-1-4614-4106-9

El Faouzi, N.-E.; Leung, H.; Kurian, A. 2011. Data fusion in intelligent transportation systems: progress and challenges – a survey, Information Fusion 12(1): 4–10. https://doi.org/10.1016/j.inffus.2010.06.001

Ernest, P.; Mazl, R.; Preucil, L. 2004. Train locator using inertial sensors and odometer, IEEE Intelligent Vehicles Symposium 2004, 14–17 June 2004, Parma, Italy, 860–865. https://doi.org/10.1109/IVS.2004.1336497

Geistler, A.; Bohringer, F. 2004. Robust velocity measurement for railway applications by fusing eddy current sensor signals, in IEEE Intelligent Vehicles Symposium 2004, 14–17 June 2004, Parma, Italy, 664–669. https://doi.org/10.1109/IVS.2004.1336463

Government of India. 2012. Handbook on Micro Controller Based Governor Fitted on Diesel Locomotives. CAMTECH/2012/M/MCBG/1.0. Centre for Advanced Maintenance Technology (CAMTECH), Ministry of Railways, Government of India. 89 p.

Government of India. 2015. Indian Railways: Lifeline of the Nation. A White Paper. Government of India. Ministry of Railways, New Delhi, India. 66 p. Available from Internet: http://www.indianrailways.gov.in/railwayboard/uploads/directorate/finance_budget/Budget_2015-16/White_Paper-_English.pdf

Gregor, R.; Lutzeler, M.; Pellkofer, M.; Siedersberger, K.-H.; Dickmanns, E. D. 2002. EMS-Vision: a perceptual system for autonomous vehicles, IEEE Transactions on Intelligent Transportation Systems 3(1): 48–59. https://doi.org/10.1109/6979.994795

Groves, P. D. 2013. Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Artech House. 776 p.

Hartwig, K.; Grimm, M.; Meyer zu Hörste, M.; Lemmer, K. 2006. Requirements for safety relevant positioning applications in rail traffic – a demonstrator for a train borne navigation platform called “DemoOrt”, in WCRR – 7th World Congress on Railway Research, 4–8 June 2006, Montreal, Canada, 1–11.

Kalman, R. E. 1960. A new approach to linear filtering and prediction problems, Journal of Basic Engineering 82(1): 35–45. https://doi.org/10.1115/1.3662552

Kaplan, E. D. 1996. Understanding GPS: Principles and Applications. Artech House.

Khaleghi, B.; Khamis, A.; Karray, F. O.; Razavi, S. N. 2013. Multisensor data fusion: a review of the state-of-the-art, Information Fusion 14(1): 28–44. https://doi.org/10.1016/j.inffus.2011.08.001

Kim, S.-B.; Bazin, J.-C.; Lee, H.-K.; Choi, K.-H.; Park S.-Y. 2011. Ground vehicle navigation in harsh urban conditions by integrating inertial navigation system, global positioning system, odometer and vision data, IET Radar, Sonar & Navigation 5(8): 814–823. https://doi.org/10.1049/iet-rsn.2011.0100

Malakar, B.; Roy, B. K. 2014. Survey of RFID applications in railway industry, in 2014 First International Conference on Automation, Control, Energy and Systems (ACES), 1–2 February 2014, Hooghy, India, 1–6. https://doi.org/10.1109/ACES.2014.6807999

Mangal, M. 2013. Specific requirements and options for CCS of Indian railways: train collision avoidance system (TCAS), in UIC Global Signalling & Telecom Conference, 21–23 March 2013, Delhi, India.

Mansukhani, L. 2014. Train collision avoidance system (TCAS), IRSTE Journal 1: 11–18.

Mirabadi, A.; Sandidzadeh, M. A.; Hosseingholian, M.; Schmid, F. 2002. Fault tolerant train navigation system via integration of GPS, balises, tachomters and Doppler radar, in 2002 AREMA Conference, 23–25 September 2002, Washington, DC, US, 1–27.

Mirabadi, A.; Schmid, F.; Mort, N. 2003. Multisensor integration methods in the development of a fault-tolerant train navigation system, The Journal of Navigation 56(3): 385–398. https://doi.org/10.1017/S0373463303002364

Mitchell, H. B. 2007. Multi-Sensor Data Fusion: an Introduction. Springer. 282 p. https://doi.org/10.1007/978-3-540-71559-7

Noureldin, A.; Karamat, T. B.; Georgy, J. 2013. Fundamentals of Inertial Navigation, Satellite-Based Positioning and their Integration. Springer. 314 p. https://doi.org/10.1007/978-3-642-30466-8

Prakash, G.; Meena, F. S.; Prakash, A. 2008. Problem in tunneling in railway projects with special reference to North Eastern region of India, in Underground Facilities for Better environment & Safety: ITA–AITES World Tunnel Congress & 34th General Assembly, 19–25 September 2008, Agra, India.

Qin, H.; Cong, L.; Sun, X. 2012. Accuracy improvement of GPS/MEMS-INS integrated navigation system during GPS signal outage for land vehicle navigation, Journal of Systems Engineering and Electronics 23(2): 256–264. https://doi.org/10.1109/JSEE.2012.00033

Ribeiro, M. I. 2004. Kalman and Extended Kalman Filters: Concept, Derivation and Properties. Institute for Systems and Robotics, University of Lisbon, Portugal. 45 p.

Santos, A. J. D.; Soares, A. R.; De Almeida Redondo, F. M.; Carvalho, N. B. 2005. Tracking trains via radio frequency systems, IEEE Transactions on Intelligent Transportation Systems 6(2): 244–258. https://doi.org/10.1109/TITS.2005.848369

Walts, E. L. 1988. Data fusion for C3I: a tutorial, in The C3I Handbook – Command Control Communications Intelligence, 217–226.

Wang, W. J.; Wang, H. Y.; Guo, J.; Liu, Q. Y.; Zhu, M. H.; Jin, X. S. 2014. Experimental investigation of adhesion coefficient of wheel/rail under the track ramp conditions, Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 228(7): 808–815. https://doi.org/10.1177/1350650114526386

Zhang, X.; Lakafosis, V.; Traille A.; Tentzeris, M. M. 2010. Performance analysis of “fast-moving” RFID tags in state-of-the-art high-speed railway systems, 2010 IEEE International Conference on RFID-Technology and Application, 17–19 June 2010, Guangzhou, China, 281–285. https://doi.org/10.1109/RFID-TA.2010.5529918

Zhao, Y. 2011. GPS/IMU Integrated System for Land Vehicle Navigation based on MEMS. Licentiate Thesis in Geodesy. KTH Royal Institute of Technology, Stockholm, Sweden. 92 p. Available from Internet: http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A446078&dswid=-246