Estimation Technique Using Covariance Information with Relation to Wavelet Transformation in Linear Discrete Stochastic Systems

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概要

• 論文の詳細を見る

• This paper proposes an estimation technique in terms of the recursive least-squares (RLS) Wiener filter by applying the wavelet transformation to the state vector generating a signal in linear discrete-time stochastic systems. The RLS Wiener filter uses the factorized covariance information of the signal and the variance of observation noise. Here, it is assumed that the observed value vector consists of subsequent scalar observed values on the time axis. This paper also examines an estimation technique in terms of the RLS Wiener filter by operating an identity matrix trans-formation to the state vector. It is advantageous that the estimation accuracy by the proposed estimation method is superior to the standard RLS Wiener filter and the estimation procedure with the identity transformation matrix.

• 鹿児島大学の論文
• 2003-03-18

著者

• Nakamori Seiichi

  Department Of Technology Faculty Of Education

関連論文

• Design of estimators using covariance information with uncertain observations in linear discrete-time distributed parameter systems
• Design of Recursive Least-Squares Fixed-Lag Smoother using Covariance Information in Linear Continuous Stochastic Systems
• Design of Recursive Wiener Fixed-Point Smoother based on Innovations Approach in Linear Discrete-Time Stochastic Systems
• Estimation Technique Using Covariance Information with Relation to Wavelet Transformation in Linear Discrete Stochastic Systems

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