Parametric identification of vehicle handling using an extended Kalman filter Online publication date: Fri, 28-Dec-2007
by Matthew C. Best
International Journal of Vehicle Autonomous Systems (IJVAS), Vol. 5, No. 3/4, 2007
Abstract: The well known extended Kalman filter – more generally used for real-time state estimation – is used here in an unorthodox fashion; a model is prescribed for the sensors alone, and the state vector is replaced by a set of unknown model parameters. With the aid of two simple tuning parameters, the system self-regulates its estimates of parameter and sensor errors, and hence smoothly identifies optimal parameter choices. The results are shown to be comparable with least-squares identification, but this method works equally well for more general nonlinear handling models, and should be well suited to any smoothly nonlinear system.
Online publication date: Fri, 28-Dec-2007
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