Using cubature Kalman filter to estimate the vehicle state
Xiaoshuai Xin, Jinxi Chen
COMPUTER MODELLING & NEW TECHNOLOGIES 2015 19(2D) 12-17
School of Automation Engineering, University of Electronic Science and Technology of China. No.2006, Xiyuan Ave, Chengdu, China
The vehicle state is of significant to examine and control vehicle performance. But some vehicle states such as vehicle velocity and side slip angle which are vital to active safety application of vehicle can not be measured directly and must be estimated instead. In this paper, a Cubature Kalman Filter (CKF) based algorithm for estimation vehicle velocity, yaw rate and side slip angle using steering wheel angle, longitudinal acceleration and lateral sensors is proposed. The estimator is designed based on a three-degree-of-freedom (3DOF) vehicle model. Effectiveness of the estimation is examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under double lane change and slalom conditions.