Sensor Fusion Algorithm Based on Extended Kalman Filter for Estimation of Ground Vehicle Dynamics

Abstract

The current vehicle stability control techniques relies on an accurate sensor information and a complete system definition, such information is not easily obtained and requires expensive sensor technology. In this work it is presented a fusion algorithm for estimating the vehicle handling dynamic states, using inertial measurements combined with Global Positioning System (GPS) information, based on the Extended Kalman Filter algorithm (EKF). The proposed method will be able to track the state of the variable vector that includes the yaw rate, lateral velocity and longitudinal velocity of the vehicle using the information of the available sensors combined with the non-linear model of the system. In order to validate the proposed sensor fusion algorithm a simulation with a high-fidelity CarSim model is carried out and its sensors are compared with Extended Kalman Filter state variables.