Improved Strong Track Filter and Its Application to Vehicle State Estimation
改进强跟踪滤波算法及其在汽车状态估计中的应用

Accurate and real time acquirement of vehicle state variables in running is of great significance to vehicle chassis control. However, these key state variables are not easy to measure directly or cheaply. An improved strong track filter (ISTF) which is much more stable is introduced in this paper. By using a nonlinear 3 degree-of-freedom vehicle model including longitudinal motion, lateral motion, and yaw motion, a state estimation algorithm was established and applied to vehicle state estimation. Comparison was made between extended Kalman filter (EKF) and ISTF. A double lane change test was carried out on Carsim and Matlab/Simulink co-simulation as well as on a real vehicle. The results showed that ISTF is better than EKF in the estimating accuracy, tracking speed, and restraining noise. It was proved that ISTF can satisfy the requirements of vehicle state estimation.