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Home > Archive>Volume 50, Issue 1, 2022 >13-21. DOI:10.11908/j.issn.0253-374x.21441
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Train Location Method Based on Line Data Information
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1.School of Electronic and Information Engineering， Beijing Jiaotong University， Beijing 100044;2.Key Laboratory of Road and Traffic Engineering of the Ministry of Education， Tongji University， Shanghai 201804;3.School of Information, University of Technology of Belfort-Montbéliard, Belfort 90000, France;4.Shanghai Municipal Engineering Construction Development Co.， Ltd.， Shanghai 200025

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TB114.3

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    Abstract:

    Aimed at the problem that the train loses satellite signal and can not be located， a dynamic time warping （DTW） based train smoothness information matching algorithm is proposed. With the help of specific sensor， the online regularity information of the train is collected. Afterward， the collected data is processed and matched by applying DTW-KF （Kalman filter）. Using the data measured by the onboard speed sensor as template variables， the online regularity measurement data is applied to do the matching. According to the optimal matching path， distortion， and matching results of DTW， the train can be positioned offline when the train cannot be located due to the loss of the satellite signals. The template data is divided into blocks （even division and quartile） to match in real-time， and the error correction of the INS （inertial navigation system） based on the matching result of the DTW algorithm can locate the train when the satellite signal loses online location information.

    Reference
    [1] DOUMIATI M， CHARARA A， VICTORINO A， et al. Vehicle dynamics estimation using Kalman filtering： experimental validation[M]. Hoboken， New Jersey： John Wiley & Sons， 2012.
    [2] MO Y， SINOPOLI B. Kalman filtering with intermittent observations： Tail distribution and critical value[J]. IEEE Transactions on Automatic Control， 2011， 57（3）： 677.
    [3] AHMAD N， GHAZILLA R A R， KHAIRI N M， et al. Reviews on various inertial measurement unit （IMU） sensor applications[J]. International Journal of Signal Processing Systems， 2013， 1（2）： 256.
    [4] GHAHREMANI E， KAMWA I. Online state estimation of a synchronous generator using unscented Kalman filter from phasor measurements units[J]. IEEE Transactions on Energy Conversion， 2011， 26（4）： 1099.
    [5] SHULTS R， ANNENKOV A. Investigation of the different weight models in Kalman filter： A case study of GNSS monitoring results[J]. Geodesy and Geodynamics， 2018， 9（3）： 220.
    [6] YANG C， KAPLAN L， BLASCH E. Performance measures of covariance and information matrices in resource management for target state estimation[J]. IEEE Transactions on Aerospace and Electronic Systems， 2012， 48（3）： 2594.
    [7] MIRZAEI F M， ROUMELIOTIS S I. A Kalman filter-based algorithm for IMU-camera calibration： Observability analysis and performance evaluation[J]. IEEE Transactions on Robotics， 2008， 24（5）： 1143.
    [8] ZHANG P， GU J， MILIOS E E， et al. Navigation with IMU/GPS/digital compass with unscented Kalman filter[C]//IEEE International Conference Mechatronics and Automation， 2005. Niagara Falls： IEEE， 2005： 1497-1502.
    [9] CHEN F， BROWN G M， SONG M. Overview of 3-D shape measurement using optical methods[J]. Optical Engineering， 2000， 39（1）： 10.
    [10] LIU L， Li W， JIA H. Method of time series similarity measurement based on dynamic time warping[J]. Computers， Materials and Continua， 2018， 57： 97.
    [11] JEONG Y S， JEONG M K， OMITAOMU O A. Weighted dynamic time warping for time series classification[J]. Pattern Recognition， 2011， 44（9）： 2231.
    [12] IZAKIAN H， PEDRY C Z W， Jamal I. Fuzzy clustering of time series data using dynamic time warping distance[J]. Engineering Applications of Artificial Intelligence， 2015， 39： 235.
    [13] QIU H， NOURA H， QIU M， et al. A user-centric data protection method for cloud storage based on invertible DWT[J]. IEEE Transactions on Cloud Computing， 2019， 9（4）： 1293.
    [14] SALVADOR S， CHAN P. Toward accurate dynamic time warping in linear time and space[J]. Intelligent Data Analysis， 2007， 11（5）： 561.
    [15] A?FALG J， KRIEGEL H P， KR?GER P， et al. Probabilistic similarity search for uncertain time series[C]//International Conference on Scientific and Statistical Database Management Berlin： Springer， Heidelberg，2009： 435-443.
    [16] ACHTELIK M， ACHTELIK M， WEISS S， et al. Onboard IMU and monocular vision based control for MAVs in unknown in-and outdoor environments[C]//2011 IEEE International Conference on Robotics and Automation.Shanghai： IEEE， 2011： 3056-3063.
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SONG Haifeng, ZHANG Minjie, ZENG Xiaoqing, YING Peiran, HE Qiao, FENG Dongliang. Train Location Method Based on Line Data Information[J].同济大学学报(自然科学版),2022,50(1):13~21

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  • Received:September 24,2021
  • Online: February 17,2022
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