Title: Simplified extended Kalman filter for automotive state estimation

Authors: Thomas A. Wenzel, Keith J. Burnham, Mike V. Blundell, R.A. Williams, Andrew Fairgrieve

Addresses: Control Theory and Applications Centre, Coventry University, Priory Street, Coventry CV1 5FB, UK. ' Control Theory and Applications Centre, Coventry University, Priory Street, Coventry CV1 5FB, UK. ' Control Theory and Applications Centre, Coventry University, Priory Street, Coventry CV1 5FB, UK ' Jaguar and Land Rover Research, Whitley, Coventry, UK. ' Jaguar and Land Rover Research, Whitley, Coventry, UK

Abstract: This paper demonstrates the implementation of an Extended Kalman Filter (EKF) technique as a model-based vehicle state estimator. In this approach, the KF is simplified such that the Kalman gain matrix is not calculated online via the Jacobian and covariance matrices, but using a table of velocity dependent approximating functions, thus reducing the computational effort. Results to date indicate that this is an effective approach, which is of significant potential benefit to the automotive industry.

Keywords: automotive control; extended Kalman filters; EKFs; state estimation; vehicle dynamics.

DOI: 10.1504/IJMIC.2008.020120

International Journal of Modelling, Identification and Control, 2008 Vol.3 No.3, pp.201 - 211

Published online: 28 Aug 2008 *

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