Vehicle State Parameter Estimation Based on Graded Series Extended Kalman Filter Method

doi:10.12068/j.issn.1005-3026.2023.08.011

Abstract

Abstract: Accurate and efficient vehicle state estimation is the prerequisite for intelligent vehicles to achieve precise control， thus there is an urgent need to carry out research on fast and accurate state estimation algorithms. To this end， a graded series extended Kalman filter (GS-EKF) method of vehicle state parameter estimation is proposed， which aims to improve the computational efficiency and robustness of the algorithm while ensuring the estimation accuracy. Firstly， based on the graded series idea， the result of the primary extended Kalman filter estimation is used as the measurable control input signal of the secondary state estimation to realize the hierarchical cascading state estimation. Then， a 3-degree-of-freedom nonlinear dynamic vehicle state parameter estimation is established which takes the steering wheel angle and longitudinal/lateral acceleration as input variables and observation variables. Finally， a joint simulation verification platform is built to compare and analyze the accuracy， robustness and efficiency of four different algorithms. The results show that the proposed algorithm can achieve the effect of particle filter in terms of accuracy and robustness， and the efficiency is 41. 2% higher than that of particle filter.

Key words: intelligent vehicle； state estimation； graded series； extended Kalman； robustness

CLC Number:

U461.1

CHEN Long ， LIU Qiao-bin， TAO Lei. Vehicle State Parameter Estimation Based on Graded Series Extended Kalman Filter Method[J]. Journal of Northeastern University(Natural Science), 2023, 44(8): 1144-1151.

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