Name
Abstract | ||
|---|---|---|
This paper investigates the state estimation problem for stochastic nonlinear differential systems with multiplicative noise. Our purpose is to combine the noise filtering properties of the Extended Kalman Filter with the global convergence properties of high-gain observers. We propose an observer-based algorithm and provide conditions under which a bound on the estimation error can be guaranteed. Simulations show that this algorithm reveals to be more efficient than the Extended Kalman Bucy filter for systems with large measurement errors. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CDC.2011.6160233 | Decision and Control and European Control Conference |
Keywords | Field | DocType |
Kalman filters,measurement errors,nonlinear control systems,observers,stochastic systems,estimation error,extended Kalman Bucy filter,extended Kalman filter,global convergence property,high gain state observer-based algorithm,measurement error,multiplicative noise,noise filtering property,state estimation problem,stochastic nonlinear differential system filter | State observer,Mathematical optimization,Extended Kalman filter,Alpha beta filter,Computer science,Control theory,Filtering problem,Kalman filter,Invariant extended Kalman filter,Ensemble Kalman filter,Nonlinear filter | Conference |
ISSN | ISBN | Citations |
0743-1546 E-ISBN : 978-1-61284-799-3 | 978-1-61284-799-3 | 3 |
PageRank | References | Authors |
0.44 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| F. Cacace | 1 | 443 | 106.96 |
| A. Germani | 2 | 401 | 52.47 |
| Pasquale Palumbo | 3 | 86 | 22.15 |