Name
Title | ||
|---|---|---|
A quantified approach of predicting suitability of using the Unscented Kalman Filter in a non-linear application |
Abstract | ||
|---|---|---|
A mathematical framework to predict the Unscented Kalman Filter (UKF) performance improvement relative to the Extended Kalman Filter (EKF) using a quantitative measure of non-linearity is presented. It is also shown that the range of performance improvement the UKF can attain, for a given minimum probability depends on the Non-linearity Indices of the corresponding system and measurement models. Three distinct non-linear estimation problems are examined to verify these relations. A launch vehicle trajectory estimation problem, a satellite orbit estimation problem and a re-entry vehicle position estimation problem are examined to verify these relations. Using these relations, a procedure is suggested to predict the estimation performance improvement offered by the UKF relative to the EKF for a given non-linear system and measurement without designing, implementing and tuning the two Kalman Filters. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.automatica.2020.109241 | Automatica |
Keywords | DocType | Volume |
Estimation theory,Non-linear observer and filter design,Tracking,Extended Kalman Filter,Unscented Kalman Filter,Non-linearity | Journal | 122 |
Issue | ISSN | Citations |
1 | 0005-1098 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanat K. Biswas | 1 | 0 | 0.34 |
| Li Qiao | 2 | 33 | 4.98 |
| Andrew G. Dempster | 3 | 577 | 71.98 |