Vehicle driving state estimation based on extended Kalman filter

Chang Fu Zong, Dan Hu, Xiao Yang, Zhao Pan, Ying Xu

Research output: Contribution to journalArticle

32 Citations (Scopus)

Abstract

A control algorithm using the extended Kalman filtration (EKF) to estimate the vehicle state was suggested. The algorithm based on a 3-DOF nonlinear vehicle model was applied to estimate the yaw rate, the longitudinal velocity, and the ride slip angle of the mass center in the vehicle driving The estimated vehicle state parameters were compared with the results from the vehicle field test. The comparison demonstrated that the EKF based algorithm can estimate quite accurately the above-mentioned vehicle driving state parameters.

Original languageEnglish
Pages (from-to)7-11
Number of pages5
JournalJilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
Volume39
Issue number1
Publication statusPublished - 1 Jan 2009

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Extended Kalman filters
State estimation

Keywords

  • Extended Kalman filtration (EKF)
  • Nonlinearity
  • State parameter estimation
  • Vehicle driving state
  • Vehicle engineering

ASJC Scopus subject areas

  • General

Cite this

Vehicle driving state estimation based on extended Kalman filter. / Zong, Chang Fu; Hu, Dan; Yang, Xiao; Pan, Zhao; Xu, Ying.

In: Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), Vol. 39, No. 1, 01.01.2009, p. 7-11.

Research output: Contribution to journalArticle

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