An efficient method for estimation of autoregressive signals in noise

Wei Xing Zheng

An efficient method for estimation of autoregressive signals in noise

School of Computer, Data & Mathematical Sciences

Research output: Chapter in Book / Conference Paper › Conference Paper

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	IEEE Internation Symposium on Circuits and Systems: Proceedings: May 23-26, 2005, International Conference Center, Kobe, Japan: ISCAS 2005
Publisher	IEEE Computer Society
Number of pages	4
ISBN (Print)	0780388348
Publication status	Published - 2005
Event	IEEE International Symposium on Circuits and Systems - Duration: 20 May 2012 → …

Conference

Conference	IEEE International Symposium on Circuits and Systems
Period	20/05/12 → …

Keywords

parameter estimation
noise
signal processing
least squares
computer simulation
convergence

Cite this

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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.",

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author = "Zheng, {Wei Xing}",

year = "2005",

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booktitle = "IEEE Internation Symposium on Circuits and Systems: Proceedings: May 23-26, 2005, International Conference Center, Kobe, Japan: ISCAS 2005",

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AU - Zheng, Wei Xing

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.

KW - parameter estimation

KW - noise

KW - signal processing

KW - least squares

KW - computer simulation

KW - convergence

UR - http://handle.uws.edu.au:8081/1959.7/40613

M3 - Conference Paper

SN - 0780388348

BT - IEEE Internation Symposium on Circuits and Systems: Proceedings: May 23-26, 2005, International Conference Center, Kobe, Japan: ISCAS 2005

PB - IEEE Computer Society

T2 - IEEE International Symposium on Circuits and Systems

Y2 - 20 May 2012

ER -

An efficient method for estimation of autoregressive signals in noise

Abstract

Conference

Keywords

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