An Iterative Method for the Identification of Multichannel Autoregressive Processes with Additive Observation Noise

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概要

• 論文の詳細を見る

• We present a new method for the identification of time-invariant multichannel autoregressive (AR) processes corrupted by additive white observation noise. The method is based on the Yule-Walker equations and identifies the autoregressive parameters from a finite set of measured data. The input signals to the underlying process are assumed to be unknown. An inverse filtering technique is used to estimate the AR parameters and the observation noise variance, simultaneously. The procedure is iterative. Computer simulation results that demonstrate the performance of the identification method are presented.

• 社団法人電子情報通信学会の論文
• 1996-05-25

著者

• Hasan Md.

  Graduate School Of Science And Technology Chiba University

• YAHAGI Takashi

  Department of Information and Computer Sciences, Chiba University

• Hasan M

  Graduate School Of Science And Technology Chiba University

• Yahagi Takashi

  Department Of Information And Computer Sciences Chiba University

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• Design of Approximate Inverse Systems Using All-Pass Networks
• An Iterative Method for the Identification of Multichannel Autoregressive Processes with Additive Observation Noise
• Estimation of Noise Variance from Noisy Measurements of AR and ARMA Systems: Application to Blind Identification of Linear Time-Invariant Systems (Special Section on Signal Processing and System Theory)

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