An efficient method for estimation of autoregressive signals in noise

Wei Xing Zheng

An efficient method for estimation of autoregressive signals in noise

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

Abstract

![CDATA[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 = "![CDATA[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.]]",

keywords = "parameter estimation, noise, signal processing, least squares, computer simulation, convergence",

author = "Zheng, {Wei Xing}",

year = "2005",

language = "English",

isbn = "0780388348",

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

publisher = "IEEE Computer Society",

note = "IEEE International Symposium on Circuits and Systems ; Conference date: 20-05-2012",

}

TY - GEN

T1 - An efficient method for estimation of autoregressive signals in noise

AU - Zheng, Wei Xing

PY - 2005

Y1 - 2005

N2 - ![CDATA[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 - ![CDATA[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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