A new method for the nonlinear transformation of means and covariances in filters and estimators.
IEEE T AUTOMAT CONTR
477 - 482.
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.
|Title:||A new method for the nonlinear transformation of means and covariances in filters and estimators|
|Keywords:||covariance matrices, estimation, filtering, missile detection and tracking, mobile robots, nonlinear filters, prediction methods, TARGET TRACKING|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
Archive Staff Only