Name
Abstract | ||
|---|---|---|
The Ensemble Kalman filter (EnKF) is a standard algorithm in oceanography and meteorology, where it has got thousands of citations. It is in these communities appreciated since it scales much better with state dimension n than the standard Kalman filter (KF). In short, the EnKF propagates ensembles with N state realizations instead of mean values and covariance matrices and thereby avoids the computational and storage burden of working on n x n matrices. Perhaps surprising, very little attention has been devoted to the EnKF in the signal processing community. In an attempt to change this, we present the EnKF in a Kalman filtering context. Furthermore, its application to nonlinear problems is compared to sigma point Kalman filters and the particle filter, so as to reveal new insights and improvements for high-dimensional filtering algorithms in general. A simulation example shows the EnKF performance in a space debris tracking application. |
Year | Venue | Keywords |
|---|---|---|
2015 | European Signal Processing Conference | Kalman filter,ensemble Kalman filter,sigma point Kalman filter,UKF,particle filter |
DocType | ISSN | Citations |
Conference | 2076-1465 | 1 |
PageRank | References | Authors |
0.39 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Roth | 1 | 31 | 8.54 |
| Carsten Fritsche | 2 | 157 | 14.72 |
| Gustaf Hendeby | 3 | 216 | 21.37 |
| Fredrik Gustafsson | 4 | 2287 | 281.33 |