High accuracy road vehicle state estimation using extended Kalman filter

This paper describes the theoretical development and evaluation of the multisensor navigation system for high speed road vehicle. The paper focuses on the design of the nonlinear process model that is able to cope with vehicle slip using multisensor data from the inertial sensors, odometry, and D-GPS. The algorithm was evaluated using a vehicle dynamics simulator built to allow the simulation of a wide variety of driving scenarios. The simulation results show that the scheme is able to significantly reduce the errors in vehicle position and orientation estimates. They also show that the scheme allows slip angle estimation and accelerometer bias estimation.