Vehicle dynamics estimation using Kalman filters

This paper deals with the application of stochastic state estimators in veh icle dynamics control. It is often unrealistic to assume that all vehicle s tates and the disturbances acting on it can be measured. System states that cannot be measured directly, can be estimated by a Kalman Filter. The idea of the Kalman filter is to implement a model of the real system in an on-b oard computer in parallel with the system itself. This paper will give 3 ex amples of this principle applied to automotive systems.