A new method for the nonlinear transformation of means and covariances in filters and estimators

@article{Julier2000ANM,
  title={A new method for the nonlinear transformation of means and covariances in filters and estimators},
  author={Simon J. Julier and Jeffrey K. Uhlmann and Hugh F. Durrant-Whyte},
  journal={IEEE Trans. Autom. Control.},
  year={2000},
  volume={45},
  pages={477-482}
}
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. 

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