An Iterative Method for the Identification of Multichannel Autoregressive Processes with Additive Observation Noise
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E79-A No.5 pp.674-680
- Publication Date
- 1996/05/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
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Md. Kamrui HASAN, Takashi YAHAGI, "An Iterative Method for the Identification of Multichannel Autoregressive Processes with Additive Observation Noise" in IEICE TRANSACTIONS on Fundamentals,
vol. E79-A, no. 5, pp. 674-680, May 1996, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e79-a_5_674/_p
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@ARTICLE{e79-a_5_674,
author={Md. Kamrui HASAN, Takashi YAHAGI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Iterative Method for the Identification of Multichannel Autoregressive Processes with Additive Observation Noise},
year={1996},
volume={E79-A},
number={5},
pages={674-680},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={May},}
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TY - JOUR
TI - An Iterative Method for the Identification of Multichannel Autoregressive Processes with Additive Observation Noise
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 674
EP - 680
AU - Md. Kamrui HASAN
AU - Takashi YAHAGI
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E79-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 1996
AB - 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.
ER -
English
