articleTop 1% cited
A new method for the nonlinear transformation of means and covariances in filters and estimators
IEEE Transactions on Automatic Control · 2000 · Vol. 45(3) · pp. 477–482
Simon Julier✉Jeffrey Uhlmann(University of Oxford)Hugh Durrant‐Whyte(University of Technology Sydney)
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.
Target Tracking and Data Fusion in Sensor NetworksInertial Sensor and NavigationScientific Research and DiscoveriesExtended Kalman filterInvariant extended Kalman filterKalman filterEstimatorControl theory (sociology)Ensemble Kalman filterCovarianceNonlinear filterFast Kalman filterMathematics
Funding
- Georgia Institute of Technology
- Else Kröner-Fresenius-Stiftung
Citations
3,686
FWCI
32.52
field-weighted impact
References
24
Percentile
100%
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References
New extension of the Kalman filter to nonlinear systems
Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997 · 5,214 citations
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