An efficient method for estimation of autoregressive signals in noise

Wei Xing Zheng

doi:10.1109/ISCAS.2005.1464867

An efficient method for estimation of autoregressive signals in noise

Wei Xing Zheng

School of Computer, Data & Mathematical Sciences

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

1 Citation (Scopus)

Abstract

The problem of estimating parameters of autoregressive (AR) signals from noisy data is studied in this paper. First, the previous improved least-squares method with direct implementation structure (called ILSD) is revisited with the purpose of establishing its mean convergence. Second, a new and efficient estimation method for noisy AR signals is presented by re-organizing the key equations derived for the ILSD method. The feature of the new scheme is that it is in an non-iterative form, so there is no convergence issue of iteration process involved. Computer simulation results are included to illustrate the performance of the new estimation scheme.

Original language	English
Title of host publication	Proceedings - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1433-1436
Number of pages	4
ISBN (Print)	9780780388345
DOIs	https://doi.org/10.1109/ISCAS.2005.1464867
Publication status	Published - 2005
Event	IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 - Kobe, Japan Duration: 23 May 2005 → 26 May 2005

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)	0271-4310

Conference

Conference	IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005
Country/Territory	Japan
City	Kobe
Period	23/05/05 → 26/05/05

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10.1109/ISCAS.2005.1464867

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Zheng, W. X. (2005). An efficient method for estimation of autoregressive signals in noise. In Proceedings - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 (pp. 1433-1436). Article 1464867 (Proceedings - IEEE International Symposium on Circuits and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.2005.1464867

@inproceedings{64a1f956298641989e1f8943b9aa5e8e,

title = "An efficient method for estimation of autoregressive signals in noise",

abstract = "The problem of estimating parameters of autoregressive (AR) signals from noisy data is studied in this paper. First, the previous improved least-squares method with direct implementation structure (called ILSD) is revisited with the purpose of establishing its mean convergence. Second, a new and efficient estimation method for noisy AR signals is presented by re-organizing the key equations derived for the ILSD method. The feature of the new scheme is that it is in an non-iterative form, so there is no convergence issue of iteration process involved. Computer simulation results are included to illustrate the performance of the new estimation scheme.",

author = "Zheng, \{Wei Xing\}",

year = "2005",

doi = "10.1109/ISCAS.2005.1464867",

language = "English",

isbn = "9780780388345",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1433--1436",

booktitle = "Proceedings - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005",

note = "IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005 ; Conference date: 23-05-2005 Through 26-05-2005",

}

Zheng, WX 2005, An efficient method for estimation of autoregressive signals in noise. in Proceedings - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005., 1464867, Proceedings - IEEE International Symposium on Circuits and Systems, Institute of Electrical and Electronics Engineers Inc., pp. 1433-1436, IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005, Kobe, Japan, 23/05/05. https://doi.org/10.1109/ISCAS.2005.1464867

An efficient method for estimation of autoregressive signals in noise. / Zheng, Wei Xing.
Proceedings - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005. Institute of Electrical and Electronics Engineers Inc., 2005. p. 1433-1436 1464867 (Proceedings - IEEE International Symposium on Circuits and Systems).

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

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PY - 2005

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N2 - The problem of estimating parameters of autoregressive (AR) signals from noisy data is studied in this paper. First, the previous improved least-squares method with direct implementation structure (called ILSD) is revisited with the purpose of establishing its mean convergence. Second, a new and efficient estimation method for noisy AR signals is presented by re-organizing the key equations derived for the ILSD method. The feature of the new scheme is that it is in an non-iterative form, so there is no convergence issue of iteration process involved. Computer simulation results are included to illustrate the performance of the new estimation scheme.

AB - The problem of estimating parameters of autoregressive (AR) signals from noisy data is studied in this paper. First, the previous improved least-squares method with direct implementation structure (called ILSD) is revisited with the purpose of establishing its mean convergence. Second, a new and efficient estimation method for noisy AR signals is presented by re-organizing the key equations derived for the ILSD method. The feature of the new scheme is that it is in an non-iterative form, so there is no convergence issue of iteration process involved. Computer simulation results are included to illustrate the performance of the new estimation scheme.

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U2 - 10.1109/ISCAS.2005.1464867

DO - 10.1109/ISCAS.2005.1464867

M3 - Conference Paper

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T3 - Proceedings - IEEE International Symposium on Circuits and Systems

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BT - Proceedings - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE International Symposium on Circuits and Systems 2005, ISCAS 2005

Y2 - 23 May 2005 through 26 May 2005

ER -

An efficient method for estimation of autoregressive signals in noise

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