Vehicle Sideslip and Roll Parameter Estimation using GPS

This paper demonstrates a method for obtaining estimates of key vehicle states using the Global Positioning System (GPS) and Inertial Navigation System (INS) sensor measurements. A Kalman filter integrates the INS sensors with GPS to provide high update estimates of the sensor biases, heading, and vehicle velocities, which can be used to calculate the vehicle slip angle. Since the INS sensors and GPS antennas are attached to the vehicle body, roll and pitch effects from the vehicle motion and road influence the measurements obtained from the sensors and GPS. This paper develops a method that incorporates these roll and pitch effects to improve the accuracy of the vehicle state and sensor bias estimates. With accurate measurements of roll angle and roll rate, it is also possible to estimate key roll parameters, such as roll stiffness and damping ratio, with a second order dynamic model. Comparison of the vehicle state estimates with those predicted by the theoretical vehicle model yields similar results. This similarity verifies that the estimation scheme is giving appropriate estimates of the states.