Name
Title | ||
|---|---|---|
A new method for the nonlinear transformation of means and covariances in filters and estimators |
Abstract | ||
|---|---|---|
This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to param- eterize the mean and covariance of a (not necessarily Gaussian) proba- bility 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 ex- ample. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/9.847726 | Automatic Control, IEEE Transactions |
Keywords | Field | DocType |
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 | Extended Kalman filter,Alpha beta filter,Fast Kalman filter,Control theory,Unscented transform,Kernel adaptive filter,Invariant extended Kalman filter,Ensemble Kalman filter,Nonlinear filter,Mathematics | Journal |
Volume | Issue | ISSN |
45 | 3 | 0018-9286 |
Citations | PageRank | References |
653 | 75.80 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julier, S.J. | 1 | 1971 | 192.03 |
| Jeffrey K. Uhlmann | 2 | 2435 | 263.94 |
| Hugh F. Durrant-whyte | 3 | 685 | 82.75 |