A new look at parameter estimation of autoregressive signals from noisy observations

Wei Xing Zheng

A new look at parameter estimation of autoregressive signals from noisy observations

Wei Xing Zheng

School of Computer, Data & Mathematical Sciences

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

2 Citations (Scopus)

Abstract

This paper is concerned with parameter estimation of autoregressive (AR) signals from noisy observations. A set of bilinear equations has been derived for noisy AR signal estimation. An analysis reveals that the derived set of bilinear equations can be efficiently solved by using the separable least-squares method. That is, estimation of the observation noise variance can be conducted separately from that of the AR parameters. Once the observation noise variance has been estimated, an estimate of the AR parameters can be easily obtained without involving any iteration procedure. It is also shown that the estimate of the observation noise variance can be improved by using an overdetermined set of bilinear equations. Numerical results are given to demonstrate the effectiveness of the proposed estimation algorithm.

Original language	English
Title of host publication	ISCAS 2006
Subtitle of host publication	2006 IEEE International Symposium on Circuits and Systems, Proceedings
Pages	3778-3781
Number of pages	4
Publication status	Published - 2006
Event	ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems - Kos, Greece Duration: 21 May 2006 → 24 May 2006

Publication series

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

Conference

Conference	ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems
Country/Territory	Greece
City	Kos
Period	21/05/06 → 24/05/06

Cite this

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title = "A new look at parameter estimation of autoregressive signals from noisy observations",

abstract = "This paper is concerned with parameter estimation of autoregressive (AR) signals from noisy observations. A set of bilinear equations has been derived for noisy AR signal estimation. An analysis reveals that the derived set of bilinear equations can be efficiently solved by using the separable least-squares method. That is, estimation of the observation noise variance can be conducted separately from that of the AR parameters. Once the observation noise variance has been estimated, an estimate of the AR parameters can be easily obtained without involving any iteration procedure. It is also shown that the estimate of the observation noise variance can be improved by using an overdetermined set of bilinear equations. Numerical results are given to demonstrate the effectiveness of the proposed estimation algorithm.",

author = "Zheng, {Wei Xing}",

year = "2006",

language = "English",

isbn = "0780393902",

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

pages = "3778--3781",

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}

Zheng, WX 2006, A new look at parameter estimation of autoregressive signals from noisy observations. in ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Proceedings., 1693450, Proceedings - IEEE International Symposium on Circuits and Systems, pp. 3778-3781, ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Kos, Greece, 21/05/06.

A new look at parameter estimation of autoregressive signals from noisy observations. / Zheng, Wei Xing.
ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems, Proceedings. 2006. p. 3778-3781 1693450 (Proceedings - IEEE International Symposium on Circuits and Systems).

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

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N2 - This paper is concerned with parameter estimation of autoregressive (AR) signals from noisy observations. A set of bilinear equations has been derived for noisy AR signal estimation. An analysis reveals that the derived set of bilinear equations can be efficiently solved by using the separable least-squares method. That is, estimation of the observation noise variance can be conducted separately from that of the AR parameters. Once the observation noise variance has been estimated, an estimate of the AR parameters can be easily obtained without involving any iteration procedure. It is also shown that the estimate of the observation noise variance can be improved by using an overdetermined set of bilinear equations. Numerical results are given to demonstrate the effectiveness of the proposed estimation algorithm.

AB - This paper is concerned with parameter estimation of autoregressive (AR) signals from noisy observations. A set of bilinear equations has been derived for noisy AR signal estimation. An analysis reveals that the derived set of bilinear equations can be efficiently solved by using the separable least-squares method. That is, estimation of the observation noise variance can be conducted separately from that of the AR parameters. Once the observation noise variance has been estimated, an estimate of the AR parameters can be easily obtained without involving any iteration procedure. It is also shown that the estimate of the observation noise variance can be improved by using an overdetermined set of bilinear equations. Numerical results are given to demonstrate the effectiveness of the proposed estimation algorithm.

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SN - 0780393902

SN - 9780780393905

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

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T2 - ISCAS 2006: 2006 IEEE International Symposium on Circuits and Systems

Y2 - 21 May 2006 through 24 May 2006

ER -

A new look at parameter estimation of autoregressive signals from noisy observations

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