Recursive Estimation Technique of Signal from Output Measurement Data in Linear Discrete-Time Systems
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概要
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The recursive least-squares filter and fixed-point smoother are designed in linear discrete-time systems. The estimators require the information of the system matrix, the observation vector and the variances of the state and white Gaussian observation noise in the signal generating model. By appropriate choices of the observation vector and the state variables, the state-space model corresponding to the ARMA (autoregressive moving average) model of order (n, m) is introduced. Here, some elements of the system matrix consist of the AR parameters. This paper proposes modified iterative technique to the existing one [1] regarding the estimation of the variance of observation noise based on the estimation methods of ARMA parameters in Refs. [2], [3]. As a result, the system matrix, the ARMA parameters and the variances of the state and observation noise are estimated from the observed value and its sampled autocovariance data of finite number. The input noise variance of the ARMA model is estimated by use of the autocovariance data and the estimates of the AR parameters and one MA parameter.
- 社団法人電子情報通信学会の論文
- 1995-05-25
著者
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NAKAMORI Seiichi
Faculty of Education, Kagoshima University
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Nakamori Seiichi
Faculty Of Education Kagoshima University
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Nakamori S
The Faculty Of Education Kagoshima University
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