A new method for the nonlinear transformation of means and covariances in filters and estimators
نویسندگان
چکیده
This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parameterize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a filter that is more accurate than an extended Kalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter. Its effectiveness is demonstrated using an example.
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T. Lefebvre (corresponding author), H. Bruyninckx and J. De Schutter are with the Department of Mechanical Engineering, Katholieke Universiteit Leuven, Celestijnenlaan 300B, B-3001 Leuven, Belgium. Tel: +32(0) 16 32 24 80. Fax: +32(0) 16 32 29 87. E-mail:[email protected] T. Lefebvre and H. Bruyninckx are, respectively, Doctoral and Postdoctoral Fellows of the Fund for Scientifi...
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ورودعنوان ژورنال:
- IEEE Trans. Automat. Contr.
دوره 45 شماره
صفحات -
تاریخ انتشار 2000