This paper presents an adaptive extended Kalman filter (EKF)-based sideslip angle estimator, which utilizes a sensor fusion concept that combines the high-rate inertial sensors measurements with the low-rate GPS velocity measurements. The sideslip angle estimation is based on a vehicle kinematic model relying on the lateral accelerometer and yaw rate gyro measurements. The vehicle velocity measurements from low-cost, single antenna GPS receiver are used for compensation of potentially large drift-like estimation errors caused by inertial sensors offsets. Adaptation of EKF state covariance matrix ensures a fast convergence of inertial sensors offsets estimates, and consequently a more accurate sideslip angle estimate. By using a detailed simulation analysis, it is found out that the main sources of estimation errors include inaccuracies of pre-estimated vehicle longitudinal velocity obtained from nondriven wheel speed sensors, the GPS velocity signal latency, and the road bank-related disturbances. Several compensation methods are proposed to suppress the influence of these errors.
Adaptive EKF-Based Estimator of Sideslip Angle Using Fusion of Inertial Sensors and GPS
Sae International Journal of Passenger Cars. Mechanical Systems
Sae Int. J. Passeng. Cars – Mech. Syst
SAE 2011 World Congress & Exhibition ; 2011
Sae International Journal of Passenger Cars - Mechanical Systems ; 4 , 1 ; 700-712
2011-04-12
13 pages
Conference paper
English
Automotive engineering | 2011
|2011-01-0953 Adaptive EKF-Based Estimator of Sideslip Angle Using Fusion of Inertial Sensors and GPS
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