Vehicle State Estimation Based on Sage–Husa Adaptive Unscented Kalman Filtering

To combat the impacts of uncertain noise on estimation vehicle state parameters and high cost sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The equation observer derived by establishing vehicle’s three degrees-of-freedom (DOF) model. On this basis, Sage–Husa algorithm (UKF) are combined to form AUKF adaptively update statistical feature measurement noise. Finally, co-simulation using Carsim Matlab/Simulink confirms effective reasonable. simulation results demonstrate that proposed algorithm, compared UKF increases accuracy 19.13%, 32.8%, 39.46% in yaw rate, side-slip angle, longitudinal velocity, respectively. This because adjusts covariance matrix, which can estimate more accurately.