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Design of Linear Continuous-Time Stochastic Estimators Using Covariance Information in Krein Spaces

Seiichi NAKAMORI

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Summary :

This paper proposes new recursive fixed-point smoother and filter using covariance information in linear continuous-time stochastic systems. To be able to treat the stochastic signal estimation problem, a performance criterion, extended from the criterion in the H filtering problem by introducing the stochastic expectation, is newly introduced in this paper. The criterion is transformed equivalently into a min-max principle in game theory, and an observation equation in the Krein spaces is obtained as a result. For γ2<, the estimation accuracies of the fixed-point smoother and the filter are superior to the recursive least-squares (RLS) Wiener estimators previously designed in the transient estimation state. Here, γ represents a parameter in the proposed criterion. This paper also presents the fixed-point smoother and the filter using the state-space parameters from the devised estimators using the covariance information.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E84-A No.9 pp.2261-2271
Publication Date
2001/09/01
Publicized
Online ISSN
DOI
Type of Manuscript
PAPER
Category
Systems and Control

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