DocumentCode
1348572
Title
A new method for the nonlinear transformation of means and covariances in filters and estimators
Author
Julier, Simon ; Uhlmann, Jeffrey ; Durrant-Whyte, Hugh F.
Author_Institution
IDAK Ind., Jefferson City, MO, USA
Volume
45
Issue
3
fYear
2000
fDate
3/1/2000 12:00:00 AM
Firstpage
477
Lastpage
482
Abstract
This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parametrize 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
Keywords
covariance matrices; discrete time systems; error analysis; estimation theory; filtering theory; missile guidance; mobile robots; nonlinear systems; probability; state estimation; Kalman filter; covariance matrix; discrete time systems; error estimation; missile tracking; mobile robots; nonlinear filters; nonlinear systems; probability distribution; state estimation; Additive noise; Covariance matrix; Filtering; Gaussian processes; Missiles; Mobile robots; Nonlinear filters; Nonlinear systems; Probability distribution; State estimation;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/9.847726
Filename
847726
Link To Document