Abstract
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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This work was supported by the National Natural Science Foundation of China (grant numbers 61663042).
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Qi, D., Feng, J., Ni, X. et al. Maximum Correntropy Extended Kalman Filter for Vehicle State Observation. Int.J Automot. Technol. 24, 377–388 (2023). https://doi.org/10.1007/s12239-023-0031-8
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DOI: https://doi.org/10.1007/s12239-023-0031-8