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

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概要

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• 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 minmax principle in game theory, and an observation equation in the Krein spaces is obtained as a result. For γ^2 < ∞, 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.

• 一般社団法人電子情報通信学会の論文
• 2001-09-01

著者

• Nakamori Seiichi

  The Faculty Of Education Kagoshima University

関連論文

• Least-Squares Linear Smoothers from Randomly Delayed Observations with Correlation in the Delay (Digital Signal Processing)
• Design of Filter Using Covariance Information in Continuous-Time Stochastic Systems with Nonlinear Observation Mechanism
• Design of Linear Continuous-Time Stochastic Estimators Using Covariance Information in Krein Spaces

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