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.


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    Title :

    Adaptive EKF-Based Estimator of Sideslip Angle Using Fusion of Inertial Sensors and GPS


    Additional title:

    Sae International Journal of Passenger Cars. Mechanical Systems
    Sae Int. J. Passeng. Cars – Mech. Syst


    Contributors:

    Conference:

    SAE 2011 World Congress & Exhibition ; 2011



    Publication date :

    2011-04-12


    Size :

    13 pages




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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