Vehicle driving state estimation based on extended Kalman filter

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

    Research output: Contribution to journalArticlepeer-review

    39 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

    Keywords

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

    ASJC Scopus subject areas

    • General

    Fingerprint

    Dive into the research topics of 'Vehicle driving state estimation based on extended Kalman filter'. Together they form a unique fingerprint.

    Cite this