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LIU Jiang, CAI Bo-gen, TANG Tao, WANG Jian. CKF-based robust filtering algorithm for GNSS/INS integrated train positioning[J]. Journal of Traffic and Transportation Engineering, 2010, 10(5): 102-107. doi: 10.19818/j.cnki.1671-1637.2010.05.018
Citation: LIU Jiang, CAI Bo-gen, TANG Tao, WANG Jian. CKF-based robust filtering algorithm for GNSS/INS integrated train positioning[J]. Journal of Traffic and Transportation Engineering, 2010, 10(5): 102-107. doi: 10.19818/j.cnki.1671-1637.2010.05.018
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CKF-based robust filtering algorithm for GNSS/INS integrated train positioning

doi: 10.19818/j.cnki.1671-1637.2010.05.018
  • 1.

    School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

  • 2.

    State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

More Information
  • Author Bio:

    LIU Jiang(1985-), male, doctoral student, +86-10-51685216, jiangliu.lj@gmail.com

    TANG Tao(1963-), male, professor, PhD, +86-10-51684228, ttang@bjtu.edu.cn

  • Received Date: 2010-06-01
  • Publish Date: 2010-10-25
    Abstract
  • Based on the fusion estimation problems of nonlinearity and robustness in integrated train positioning, the principle and architecture of global navigation satellite system/inertial navigation system(GNSS/INS)integrated train positioning system were introduced, the scheme of CKF nonlinear filter approach was given, and a novel CKF-based robust filtering algorithm was presented by applying H∞ robust filter theory to standard CKF frame. The autonomous filter state monitoring was discussed for the proposed algorithm. The filter convergence criterion and the robustness criterion of error's H∞ norm bound were analyzed, and field tests in Qinghai-Tibet Railway were illustrated. Analysis result shows that under field test condition, the average estimation error of the proposed algorithm decreases by 7.13% and 4.85% respectively in comparison with standard UKF and CKF. The root mean square of estimation error increases by 8.56% with constraint level from 31.5 to 1 315.0, so the estimation precision changes mildly, and the algorithm is effective.

     

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