Journal of Shanghai Jiaotong University (Science)

, Volume 24, Issue 1, pp 130–136 | Cite as

Particle Filter and Its Application in the Integrated Train Speed Measurement

  • Liang Zhang (张梁)
  • Qilian Bao (鲍其莲)Email author
  • Ke Cui (崔科)
  • Yaodong Jiang (蒋耀东)
  • Haigui Xu (徐海贵)
  • Yuding Du (杜雨丁)


Particle filter (PF) can solve the problem of state estimation under strong non-linear non-Gaussian noise condition with respect to traditional Kalman filter (KF) and those improved KFs such as extended KF (EKF) and unscented KF (UKF). However, problems such as particle depletion and particle degradation affect the performance of PF. Optimizing the particle set to high likelihood region with intelligent optimization algorithm results in a more reasonable distribution of the sampling particles and more accurate state estimation. In this paper, a novel bird swarm algorithm based PF (BSAPF) is presented. Firstly, different behavior models are established by emulating the predation, flight, vigilance and follower behavior of the birds. Then, the observation information is introduced into the optimization process of the proposal distribution with the design of fitness function. In order to prevent particles from getting premature (being stuck into local optimum) and increase the diversity of particles, Lévy flight is designed to increase the randomness of particle’s movement. Finally, the proposed algorithm is applied to estimate the speed of the train under the condition that the measurement noise of the wheel sensor is non-Gaussian distribution. Simulation study and experimental results both show that BSAPF is more accurate and has more effective particle number as compared with PF and UKF, demonstrating the promising performance of the method.

Key words

particle filter (PF) bird swarm algorithm fitness function Lévy flight proposal distribution integrated train speed measurement 

CLC number

TP 273 

Document code


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Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Liang Zhang (张梁)
    • 1
    • 2
    • 3
  • Qilian Bao (鲍其莲)
    • 1
    • 2
    Email author
  • Ke Cui (崔科)
    • 4
  • Yaodong Jiang (蒋耀东)
    • 4
  • Haigui Xu (徐海贵)
    • 4
  • Yuding Du (杜雨丁)
    • 1
    • 2
  1. 1.Department of Instrument Science and Engineering, School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment InstrumentShanghaiChina
  3. 3.Xichang Satellite Launch CenterXichangChina
  4. 4.Department of Research and DevelopmentCASCO Signal Ltd.ShanghaiChina

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