Name
Abstract | ||
|---|---|---|
In state estimation theory, two directions are mainly followed in order to model disturbances and errors. Either uncertainties are modeled as stochastic quantities or they are characterized by their membership to a set. Both approaches have distinct advantages and disadvantages making each one inherently better suited to model different sources of estimation uncertainty. This paper is dedicated to the task of combining stochastic and set-membership estimation methods. A Kalman gain is derived that minimizes the mean squared error in the presence of both stochastic and additional unknown but bounded uncertainties, which are represented by Gaussian random variables and ellipsoidal sets, respectively. As a result, a generalization of the well-known Kalman filtering scheme is attained that reduces to the standard Kalman filter in the absence of set-membership uncertainty and that otherwise becomes the intersection of sets in case of vanishing stochastic uncertainty. The proposed concept also allows to prioritize either the minimization of the stochastic uncertainty or the minimization of the set-membership uncertainty. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CDC.2012.6426132 | Decision and Control |
Keywords | Field | DocType |
Gaussian processes,Kalman filters,mean square error methods,minimisation,random processes,state estimation,Gaussian random variable,Kalman filtering,bounded uncertainties,ellipsoidal set,estimation uncertainty,mean squared error,minimization,optimal Kalman gain,set-membership state estimation,set-membership uncertainty,stochastic state estimation,stochastic uncertainty | Mathematical optimization,Extended Kalman filter,Fast Kalman filter,Computer science,Stochastic process,Mean squared error,Kalman filter,Gaussian process,Estimation theory,Invariant extended Kalman filter | Conference |
ISSN | ISBN | Citations |
0743-1546 E-ISBN : 978-1-4673-2064-1 | 978-1-4673-2064-1 | 2 |
PageRank | References | Authors |
0.41 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Noack | 1 | 168 | 23.73 |
| Florian Pfaff | 2 | 15 | 9.01 |
| Uwe D. Hanebeck | 3 | 599 | 71.02 |