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Switching LDS detection for GNSS-based train integrity monitoring system

Switching LDS detection for GNSS-based train integrity monitoring system

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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).

References

    1. 1)
      • S. Zhang . (2008)
        1. Zhang, S.: ‘General technical scheme of CTCS-3 train control system’ (China Railway Publishing House Press, Beijing, 2008).
        .
    2. 2)
      • J. Beugin , A. Filip , J. Marais .
        2. Beugin, J., Filip, A., Marais, J., et al: ‘Galileo for railway operations: question about the positioning performances analogy with the RAMS requirements allocated to safety applications’, Eur. Transp. Res. Rev., 2010, 2, (2), pp. 93102.
        . Eur. Transp. Res. Rev. , 2 , 93 - 102
    3. 3)
      • A. Filip , H. Mocek , L. Bazant .
        3. Filip, A., Mocek, H., Bazant, L.: ‘GPS/GNSS based train positioning for safety critical applications’, Signal Draht, 2001, 93, (5), pp. 5155.
        . Signal Draht , 5 , 51 - 55
    4. 4)
      • A. Simsky , F. Wilms , J. Franckart .
        4. Simsky, A., Wilms, F., Franckart, J.: ‘GNSS-based failsafe train positioning system for low-density traffic lines based on one dimensional positioning algorithm’. Proc. Conf. NAVITEC, Noordwijk, Netherland, December 2004, pp. 18.
        . Proc. Conf. NAVITEC , 1 - 8
    5. 5)
      • M. Wegener , E. Schnieder .
        5. Wegener, M., Schnieder, E.: ‘A measurement standard for vehicle localization and its ISO-compliant measurement uncertainty evaluation’, IEEE Trans. Instrum. Meas., 2012, 61, (11), pp. 30033013.
        . IEEE Trans. Instrum. Meas. , 11 , 3003 - 3013
    6. 6)
      • H. Scholten , R. Westenberg , M. Schoemaker .
        6. Scholten, H., Westenberg, R., Schoemaker, M.: ‘Trainspotting, a WSN-based train integrity system’. Proc. Conf. Networks, Washington, USA, March 2009, pp. 226231.
        . Proc. Conf. Networks , 226 - 231
    7. 7)
      • H. Scholten , R. Westenberg , M. Schoemaker .
        7. Scholten, H., Westenberg, R., Schoemaker, M.: ‘Sensing train integrity’. Proc. Conf. Sensors, Canterbury, New Zealand, October 2009, pp. 669674.
        . Proc. Conf. Sensors , 669 - 674
    8. 8)
      • Y. Sehchan , Y. Yoon , K. Kim .
        8. Sehchan, Y., Yoon, Y., Kim, K., et al: ‘Design of train integrity monitoring system for radio based train control system’. Proc. Conf. Control, Automation and Systems, JeJu Island, Korea, October 2012, pp. 12371240.
        . Proc. Conf. Control, Automation and Systems , 1237 - 1240
    9. 9)
      • A. Acharya , S. Sadhu , T. Ghoshal .
        9. Acharya, A., Sadhu, S., Ghoshal, T.: ‘Train localization and parting detection using data fusion’, Transportation Research Part C: Emerging Technologies, 2011, 19, (1), pp. 7584.
        . Transportation Research Part C: Emerging Technologies , 1 , 75 - 84
    10. 10)
      • A. Neri , F. Rispoli , P. Salvatori .
        10. Neri, A., Rispoli, F., Salvatori, P.: ‘A train integrity solution based on GNSS double-difference approach’. ION GNSS + , Tampa, USA, September 2014, pp. 3450.
        . , 34 - 50
    11. 11)
      • Y. An , B. Cai , B. Ning .
        11. An, Y., Cai, B., Ning, B., et al: ‘Research on train integrity monitoring method based on GPS and virtual-satellite’, Journal of China Railway Society, 2012, 34, (9), pp. 4044.
        . Journal of China Railway Society , 9 , 40 - 44
    12. 12)
      • D. Barber . (2012)
        12. Barber, D.: ‘Bayesian reasoning and machine learning’ (Cambridge University Press, London, 2012).
        .
    13. 13)
      • V. Pavlovic , J. Rehg , J. MacCormick .
        13. Pavlovic, V., Rehg, J., MacCormick, J.: ‘Learning switching linear models of human motion’. Conf. Neural Information Processing Systems, Denver, USA, March 2000, pp. 981987.
        . Conf. Neural Information Processing Systems , 981 - 987
    14. 14)
      • M. Sang , J. Rehg , T. Balch .
        14. Sang, M., Rehg, J., Balch, T., et al: ‘Learning and inferring motion patterns using parametric segmental switching linear dynamic systems’, Int. Journal of Computer Vision, 2008, 1, (3), pp. 103124.
        . Int. Journal of Computer Vision , 3 , 103 - 124
    15. 15)
      • 15. AN10-1: ‘Annex 10 (Aeronautical Telecommunications) To The Convention On Interna tional Civil Aviation, Volume I - Radio Navigation Aids, International Standards And Recommended Practices (SARPs)’, 2001.
        .
    16. 16)
      • M. Wegener , M. Brahmi , K. Siedersberger .
        16. Wegener, M., Brahmi, M., Siedersberger, K.: ‘Requirements on reference systems for vehicle localisation’, ATZelektronik worldwide, 2012, 7, (5), pp. 5863.
        . ATZelektronik worldwide , 5 , 58 - 63
    17. 17)
      • D. Lu , E. Schnieder .
        17. Lu, D., Schnieder, E.: ‘Performance evaluation of GNSS for train localization’, IEEE Transaction on Intelligent Transportation System, 2015, 16, (2), pp. 10541059.
        . IEEE Transaction on Intelligent Transportation System , 2 , 1054 - 1059
    18. 18)
      • M. West , J. Harrison . (1997)
        18. West, M., Harrison, J.: ‘Bayesian forecasting and dynamic models’ (Springer Press, Berlin, 1997).
        .
    19. 19)
      • V. Pavlovic , J. Rehg , T. Cham .
        19. Pavlovic, V., Rehg, J., Cham, T., et al: ‘A dynamic bayesian network approach to figure tracking using learned dynamic models’. Proc, Conf. Computer Vision, Corfu, Greece, September 1999, pp. 94101.
        . Proc, Conf. Computer Vision , 94 - 101
    20. 20)
      • V. Pavlovic , J. Rehg , T. Cham .
        20. Pavlovic, V., Rehg, J., Cham, T., et al: ‘A dynamic bayesian network approach to tracking using learned switching dynamic models’. Proc, Conf. Hybrid Systems: Computation and Control, Pittsburgh, USA, September 2000, pp. 366380.
        . Proc, Conf. Hybrid Systems: Computation and Control , 366 - 380
    21. 21)
      • M. Sang , J. Rehg , T. Balch .
        21. Sang, M., Rehg, J., Balch, T., et al: ‘Data-driven MCMC for learning and inference in switching linear dynamic systems’. Proc. Conf. Artificial Intelligence, Pittsburgh, USA, 2005, vol. 20, no. 2, pp. 944949.
        . Proc. Conf. Artificial Intelligence , 2 , 944 - 949
    22. 22)
      • M. Sang , A. Ranganathan , J. Rehg .
        22. Sang, M., Ranganathan, A., Rehg, J., et al: ‘A variational inference method for switching linear dynamic systems’. GVU Technical Report, GIT-GVU-05-16, 2005.
        .
    23. 23)
      • Z. Ghahramani , G. Hinton .
        23. Ghahramani, Z., Hinton, G.: ‘Parameter estimation for linear dynamical systems’. Technical report, University of Toronto, 1996.
        .
    24. 24)
      • U. Lerner , R. Parr .
        24. Lerner, U., Parr, R.: ‘Inference in hybrid networks: theoretical limits and practical algorithms’. Proc. Conf. Artificial Intelligence, San Francisco, USA, 2001, pp. 310318.
        . Proc. Conf. Artificial Intelligence , 310 - 318
    25. 25)
      • C. Robert , G. Casella . (2013)
        25. Robert, C., Casella, G.: ‘Monte carlo statistical methods’ (Springer Science & Business Media Press, Berlin, 2013).
        .
    26. 26)
      • A. Doucet , N. Gordon , V. Krishnamurthy .
        26. Doucet, A., Gordon, N., Krishnamurthy, V.: ‘Particle filters for state estimation of jump Markov linear systems’, IEEE Transaction on Signal Processing, 2001, 49, (3), pp. 613624.
        . IEEE Transaction on Signal Processing , 3 , 613 - 624
    27. 27)
      • K. Murphy , S. Russell .
        27. Murphy, K., Russell, S.: ‘Rao-blackwellised particle filtering for dynamic bayesian networks’. Proc. Conf. Sequential Monte Carlo methods in practice, San Francisco, USA, 2001, pp. 499515.
        . Proc. Conf. Sequential Monte Carlo methods in practice , 499 - 515
    28. 28)
      • N. Freitas , R. Dearden , F. Hutter .
        28. Freitas, N., Dearden, R., Hutter, F., et al: ‘Diagnosis by a waiter and a mars explorer’, Proc of IEEE, 2004, 92, (3), pp. 455468.
        . Proc of IEEE , 3 , 455 - 468
    29. 29)
      • Z. Ghahramani , G. Hinton .
        29. Ghahramani, Z., Hinton, G.: ‘Variational Learning for Switching State-Space Models’, Neural Comput., 2000, 12, (4), pp. 831864.
        . Neural Comput. , 4 , 831 - 864
    30. 30)
      • O. Zoeter , T. Heskes .
        30. Zoeter, O., Heskes, T.: ‘Hierarchical visualization of time-series data using switching linear dynamical systems’, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2003, 25, (10), pp. 12011214.
        . IEEE Transaction on Pattern Analysis and Machine Intelligence , 10 , 1201 - 1214
    31. 31)
      • 31. Federal Railroad Administration Office of Safety Analysis’, http://safetydata.fra.dot.gov/OfficeofSafety, accessed October2015.
        .
    32. 32)
      • P. Howlett , P. Pudney . (2012)
        32. Howlett, P., Pudney, P.: ‘Energy-efficient train control’ (Springer Science & Business Media Press, Berlin, 2012).
        .
    33. 33)
      • S. Su , T. Tang , C. Roberts .
        33. Su, S., Tang, T., Roberts, C.: ‘A cooperative train control model for energy saving’, IEEE Transaction on Intelligent Transportation Systems, 2015, 16, (2), pp. 622631.
        . IEEE Transaction on Intelligent Transportation Systems , 2 , 622 - 631
    34. 34)
      • Z. Sun . (2005)
        34. Sun, Z.: ‘Study of train traction calculation’ (Railroad Publication House of China. Press, Beijing, 2005).
        .
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