For vehicle state estimation, the conventional Kalman filter performs well under the Gaussian assumption, but in the real non-Gaussian situation (especially when the noise is non-Gaussian heavy-tailed), it shows poor accuracy and robustness. In this paper, an extended Kalman filter (EKF) algorithm based on the maximum correntropy criterion (MCC) is proposed (MCCEKF), and a lateral-longitudinal coupled vehicle model is established, while a state observer containing the yaw rate, vehicle sideslip angle, and longitudinal vehicle speed is designed using the easily available measurement information of on-board sensors. After analyzing the complexity of the proposed algorithm, the new algorithm is verified on the Simulink/CarSim simulation experimental platform by Double Lane Change and Sine Sweep Steer Torque Input maneuver. Experimental results show that the MCC-based EKF algorithm has stronger robustness and better estimation accuracy than the traditional EKF algorithm in the case of non-Gaussian noise, and the MCCEKF is more applicable for vehicle state estimation in practical situations.


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    Title :

    Maximum Correntropy Extended Kalman Filter for Vehicle State Observation


    Additional title:

    Int.J Automot. Technol.


    Contributors:
    Qi, Dengliang (author) / Feng, Jingan (author) / Ni, Xiangdong (author) / Wang, Lei (author)

    Published in:

    Publication date :

    2023-04-01


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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