| 제목 |
MAXIMUM CORRENTROPY EXTENDED KALMAN FILTER FOR VEHICLE STATE OBSERVATION
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| 분야 |
Electrical and Electronics |
| 언어 |
English |
| 저자 |
Dengliang Qi(Shihezi University), Jingan Feng(Shihezi University), Xiangdong Ni(Shihezi University), Lei Wang(Shihezi University) |
| Key Words |
Vehicle state estimation, Maximum correntropy criterion, Non-Gaussian noise, Vehicle dynamics |
| 초록 |
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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