Recursive Estimation Technique of Signal from Output Measurement Data in Linear Discrete-Time Systems

• NAKAMORI Seiichi

  Faculty of Education, Kagoshima University

抄録

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.

収録刊行物

• IEICE transactions on fundamentals of electronics, communications and computer sciences

  IEICE transactions on fundamentals of electronics, communications and computer sciences 78 (5), 600-607, 1995-05-25

  一般社団法人電子情報通信学会

キーワード

• digital signal processing
• linear discrete-time systems
• autocovariance data
• ARMA model

詳細情報

• CRID: 1573950402123602176
• NII論文ID: 110003207518
• NII書誌ID: AA10826239
• ISSN: 09168508
• 本文言語コード: en
• データソース種別:
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