A new method for the nonlinear transformation of means and covariances in filters and estimators
Simon Julier, Jeffrey K. Uhlmann, Hugh F Durrant-Whyte · IEEE Transactions on Automatic Control · 2000
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.