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<p>Sideslip angle, yaw rate, and vehicle velocity are essential for intelligent vehicle control. Since these vehicle states are not measured directly, some Kalman-based approaches have been developed to estimate these states using in-vehicle sensors. However, the existing studies seldom account for the influence of sensor data loss on estimation accuracy. In addition, the process and measurement noise change during the estimation process because of the various driving conditions. To address these problems, an expectation-maximization robust extended Kalman filter (EMREKF) is proposed. Firstly, a robust extended Kalman filter (REKF) is developed to deal with the impact of missing measurements. Then, an improved expectation maximization (EM) algorithm that considers data loss is presented to update the noise parameter of the REKF dynamically. Finally, the improved EM is fused with the REKF to form the EMREKF to estimate vehicle state. The experimental results demonstrate that the EMREKF outperforms EKF, REKF, and maximum correntropy criterion EKF for various degrees of data loss and the proposed algorithm has a strong adaptive ability to different driving conditions.</p>

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A Bayesian expectation maximization algorithm for state estimation of intelligent vehicles considering data loss and noise uncertainty

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Yan Wang,
  • Feng Tian,
  • Jianqiang Wang,
  • Keqiang Li

摘要

Sideslip angle, yaw rate, and vehicle velocity are essential for intelligent vehicle control. Since these vehicle states are not measured directly, some Kalman-based approaches have been developed to estimate these states using in-vehicle sensors. However, the existing studies seldom account for the influence of sensor data loss on estimation accuracy. In addition, the process and measurement noise change during the estimation process because of the various driving conditions. To address these problems, an expectation-maximization robust extended Kalman filter (EMREKF) is proposed. Firstly, a robust extended Kalman filter (REKF) is developed to deal with the impact of missing measurements. Then, an improved expectation maximization (EM) algorithm that considers data loss is presented to update the noise parameter of the REKF dynamically. Finally, the improved EM is fused with the REKF to form the EMREKF to estimate vehicle state. The experimental results demonstrate that the EMREKF outperforms EKF, REKF, and maximum correntropy criterion EKF for various degrees of data loss and the proposed algorithm has a strong adaptive ability to different driving conditions.