Authors: Matthew C. Best
Addresses: Department of Aeronautical and Automotive Engineering, Loughborough University, Leicestershire, LE11 3TU, UK
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.
Keywords: vehicle handling dynamics; extended Kalman filter; system identification; vehicle parameters; sensor errors; parameter estimation.
International Journal of Vehicle Autonomous Systems, 2007 Vol.5 No.3/4, pp.256 - 273
Available online: 28 Dec 2007 *Full-text access for editors Access for subscribers Purchase this article Comment on this article