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State Estimation for Ellipsoidally Constrained Dynamic Systems with Set-membership Pseudo Measurements

Noack, Benjamin 1; Baum, M. 1; Hanebeck, U. D. 1
1 Institut für Anthropomatik und Robotik (IAR), Karlsruher Institut für Technologie (KIT)

Abstract:

In many dynamic systems, the evolution of the
state is subject to specific constraints. In general, constraints
cannot easily be integrated into the prediction-correction structure
of the Kalman filter algorithm. Linear equality constraints
are an exception to this rule and have been widely used and
studied as they allow for simple closed-form expressions. A
common approach is to reformulate equality constraints into
pseudo measurements of the state to be estimated. However,
equality constraints define deterministic relationships between
state components which is an undesirable property in Kalman
filtering as this leads to singular covariance matrices. A second
problem relates to the knowledge required to identify and
define precise constraints, which are met by the system state.
In this article, ellipsoidal constraints are introduced that can
be employed to model a bounded region, to which the system
state is constrained. This concept constitutes an easy-to-use
relaxation of equality constraints. In order to integrate ellipsoidal
constraints into the Kalman filter structure, a generalized filter
framework is utilized that relies on a combined stochastic and
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Originalveröffentlichung
DOI: 10.1109/MFI.2015.7295824
Scopus
Zitationen: 7
Dimensions
Zitationen: 5
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2015
Sprache Englisch
Identifikator ISBN: 978-1-4799-7772-7
KITopen-ID: 1000051033
Erschienen in Proceedings of the 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2015), 14-16 Sept. 2015, San Diego, CA, USA
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 297-302
Nachgewiesen in Dimensions
Scopus
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