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State Estimation with Sets of Densities considering Stochastic and Systematic Errors

Noack, Benjamin; Klumpp, Vesa; Hanebeck, Uwe D.

In practical applications, state estimation requires the consideration of stochastic and systematic errors. If both error types are present, an exact probabilistic description of the state estimate is not possible, so that common Bayesian estimators have to be questioned. This paper introduces a theoretical concept, which allows for incorporating unknown but bounded errors into a Bayesian inference scheme by utilizing sets of densities. In order to derive a tractable estimator, the Kalman filter is applied to ellipsoidal sets of means, which are used to bound additive systematic errors. Also, an extension to nonlinear system and observation models with ellipsoidal error bounds is presented. The derived estimator is motivated by means of two example applications.

Volltext §
DOI: 10.5445/IR/1000029061
Zitationen: 30
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2009
Sprache Englisch
Identifikator urn:nbn:de:swb:90-290611
KITopen-ID: 1000029061
Erschienen in Proceedings of the 12th International Conference on Information Fusion (Fusion 2009). Seattle, Washington, USA, 06.- 09.07.2009
Seiten 1751-17-58
Nachgewiesen in Scopus
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