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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2014  >  14(3): 52-57
DING Jian-ming, LIN Jian-hui, WANG Han, HUANG Chen-guang, ZHAO Jie. Performance parameter estimation method of high-speed train based on Rao-Blackwellised particle filter[J]. Journal of Traffic and Transportation Engineering, 2014, 14(3): 52-57.
Citation: DING Jian-ming, LIN Jian-hui, WANG Han, HUANG Chen-guang, ZHAO Jie. Performance parameter estimation method of high-speed train based on Rao-Blackwellised particle filter[J]. Journal of Traffic and Transportation Engineering, 2014, 14(3): 52-57.
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Performance parameter estimation method of high-speed train based on Rao-Blackwellised particle filter

  • 1.

    State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

  • 2.

    CSR Sifang Co., Ltd., Qingdao 266111, Shandong, China

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  • Author Bio:

    DING Jian-ming (1981-), male, assistant researcher, PhD, +86-28-87600558, fdingjianming@126.com

  • Received Date: 2014-02-07
  • Publish Date: 2014-06-25
    Abstract
  • In order to solve the nonlinear problems caused in augmenting the state vector of the performance parameters of high-speed train, a method of parameter estimation based on RaoBlackwellised particle filter (RBPF) was developed.Under the framework of Rao-Blackwellised (RB) principle, the probabilistic model of parameter estimation was divided.The Kalman filter (KF) was applied for the prediction time step and measurement update of linear state block and the RBPF was applied for the prediction time step and measurement update of nonlinear parameter block to realize the dynamic estimation.Through theoretical analysis and parameter estimation example of high-speed train, the validity of RBPF method for parameter dynamic estimation was verified.Analysis result shows that compared with the classical extended KF (EKF) method, RBPF method has the characteristics of initial immunity and algorithm stability.RBPF method shows its good engineering applicability for the minor parameter estimation error which is less than 5%, and inexistence of estimation deviation.

     

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