Build and evaluate state estimation Models using EKF and UKF

Abstract: In vehicle control practice, there are some variables, such as lateral tire force, body slip angle and yaw rate, that cannot or is hard to be measured directly and accurately. Vehicle model, like the bicycle model, offers an alternative way to get them indirectly, however due to the widely existent simplification and inaccuracy of vehicle models, there are always biases and errors in prediction from them. When developing advanced vehicle control functions, it is necessary and significant to know these variables in relatively high precision. Kalman filter offers a choice to estimate these variables accurately with measurable variables and with vehicle model together. In this thesis, estimation models based on Extended Kalman Filter (EKF) and Uncented Kalman Filter (UKF) are built separately to evaluate the lateral tire force, body slip angel and yaw rate of two typical passenger vehicles. Matlab toolbox EKF/UKF developed by Simo Särkkä, et al. is used to implement the estimation models. By comparing their principle, algorithm and results, the better one for vehicle state estimation will be chosen and justified. The thesis is organized in the following 4 parts: First, EKF and UKF are studied from their theory and features. Second, vehicle model used for prediction in Kalman filter is build and justified. Third, algorithms of EKF and UKF for this specific case are analysed. EKF and UKF are then implemented based on the algorithms with the help of Matlab toolbox EKF/UKF. Finally, comparisons between EKF and UKF are presented and discussed.