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Vehicle State Estimation Based on Strong Tracking Central Different Kalman Filter

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  • Yingjie Liu
  • Qijiang Xu
  • Jingxia Sun
  • Fapeng Shen
  • Dawei Cui

Abstract

Vehicle active safety control was a key technology to avoid serious safety accidents, and accurate acquisition of vehicle states signals was a necessary prerequisite to achieve active vehicle safety control. Based on the purpose, a 3-DOF nonlinear vehicle dynamics model containing constant noise and a nonlinear tire model were established, and several vehicle key states were estimated by a strong tracking central different Kalman filter (CDKF). The conclusion showed that the proposed estimator had higher accuracy and less computation requirement than the CKF, CDKF, and UKF estimators. Numerical simulation and experiments indicated that the proposed vehicle state estimation method not only had higher estimation accuracy but also had higher real-time function.

Suggested Citation

  • Yingjie Liu & Qijiang Xu & Jingxia Sun & Fapeng Shen & Dawei Cui, 2021. "Vehicle State Estimation Based on Strong Tracking Central Different Kalman Filter," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-20, August.
    • Handle: RePEc:hin:jnlmpe:4126961
    DOI: 10.1155/2021/4126961
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