On the asymptotic stability of minimum-variance unbiased input and state estimation

In this note, we investigate the asymptotic stability of the filter for minimum-variance unbiased input and state estimation developed by Gillijns and De Moor. Sufficient conditions for the stability are proposed and proven, with inspiration from the Kalman filter stability analysis.

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Bibliographic Details
Published in:	Automatica (Oxford) Vol. 48; no. 12; pp. 3183 - 3186
Main Authors:	Fang, Huazhen, de Callafon, Raymond A.
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01-12-2012 Elsevier
Subjects:	Applied sciences Asymptotic properties Asymptotic stability Automation Computer science; control theory; systems Control system analysis Control theory. Systems Exact sciences and technology Input estimation Inspiration Kalman filters Minimum-variance unbiased estimation Modelling and identification Stability Stability analysis State estimation Minimum-variance unbiased estimation Asymptotic stability Input estimation State estimation Minimal variance Unbiased estimation Kalman filter System identification
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Summary:	In this note, we investigate the asymptotic stability of the filter for minimum-variance unbiased input and state estimation developed by Gillijns and De Moor. Sufficient conditions for the stability are proposed and proven, with inspiration from the Kalman filter stability analysis.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0005-1098 1873-2836
DOI:	10.1016/j.automatica.2012.08.039