An Enhanced Adaptive Unscented Kalman Filter for Vehicle State Estimation

Abstract

Accurate vehicle state information is crucial for safe driving and dynamic control of vehicles. Vehicle state estimation under unknown noise conditions is an important research topic. A state estimation method based on enhanced adaptive unscented Kalman filter (EAUKF) is proposed to solve vehicle estimation under unknown noise conditions. The general exponential attenuation adaptive Kalman filter algorithm does not attenuate the historical data enough when the noise statistics change rapidly, leading to the state variable's inaccurate estimation. To improve the estimation accuracy of vehicle state variables, the exponential attenuation factor B was further designed according to the variation of noise variance, and the influence of the latest data on state estimation was more considered. Based on the longitudinal dynamics modeling, the EAUKF method is applied to vehicle state estimation. Compared with the standard exponential weighted adaptive Kalman filtering algorithm and the average weighted adaptive Kalman filtering algorithm, the state variable estimation accuracy of the vehicle in this paper is improved.