A new estimation algorithm for AR signals measured in noise

Wei Xing Zheng; Xiaofang Tang; Baozong Yuan

A new estimation algorithm for AR signals measured in noise

Wei Xing Zheng, Xiaofang Tang, Baozong Yuan

School of Computer, Data & Mathematical Sciences

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

Abstract

Albeit several least-squares (LS) based methods have been developed for noisy autoregressive (AR) signal identification, none is "in closed form", in that an iterative procedure is needed for estimating the AR parameters and the measurement noise variance alternately. A new formulation with respect to the measurement noise variance is presented, leading to the development of a new estimation algorithm for noisy AR signals. In addition to the eminent algorithmic difference from its predecessors, the developed algorithm achieves a better estimation accuracy while requiring an almost identical amount of computation.

Original language	English
Title of host publication	Proceedings of the 6th International Conference on Signal Processing
Publisher	IEEE Press
Number of pages	4
ISBN (Print)	0780374886
Publication status	Published - 2002
Event	International Conference on Signal Processing - Duration: 1 Jan 2010 → …

Conference

Conference	International Conference on Signal Processing
Period	1/01/10 → …

Keywords

signal processing
least squares
parameter estimation
noise
algorithms
autoregressive processes

Cite this

@inproceedings{c293553eb42d4337b6b2af1c2230ed7d,

title = "A new estimation algorithm for AR signals measured in noise",

abstract = "Albeit several least-squares (LS) based methods have been developed for noisy autoregressive (AR) signal identification, none is {"}in closed form{"}, in that an iterative procedure is needed for estimating the AR parameters and the measurement noise variance alternately. A new formulation with respect to the measurement noise variance is presented, leading to the development of a new estimation algorithm for noisy AR signals. In addition to the eminent algorithmic difference from its predecessors, the developed algorithm achieves a better estimation accuracy while requiring an almost identical amount of computation.",

keywords = "signal processing, least squares, parameter estimation, noise, algorithms, autoregressive processes",

author = "Zheng, {Wei Xing} and Xiaofang Tang and Baozong Yuan",

year = "2002",

language = "English",

isbn = "0780374886",

booktitle = "Proceedings of the 6th International Conference on Signal Processing",

publisher = "IEEE Press",

note = "International Conference on Signal Processing ; Conference date: 01-01-2010",

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TY - GEN

T1 - A new estimation algorithm for AR signals measured in noise

AU - Zheng, Wei Xing

AU - Tang, Xiaofang

AU - Yuan, Baozong

PY - 2002

Y1 - 2002

N2 - Albeit several least-squares (LS) based methods have been developed for noisy autoregressive (AR) signal identification, none is "in closed form", in that an iterative procedure is needed for estimating the AR parameters and the measurement noise variance alternately. A new formulation with respect to the measurement noise variance is presented, leading to the development of a new estimation algorithm for noisy AR signals. In addition to the eminent algorithmic difference from its predecessors, the developed algorithm achieves a better estimation accuracy while requiring an almost identical amount of computation.

AB - Albeit several least-squares (LS) based methods have been developed for noisy autoregressive (AR) signal identification, none is "in closed form", in that an iterative procedure is needed for estimating the AR parameters and the measurement noise variance alternately. A new formulation with respect to the measurement noise variance is presented, leading to the development of a new estimation algorithm for noisy AR signals. In addition to the eminent algorithmic difference from its predecessors, the developed algorithm achieves a better estimation accuracy while requiring an almost identical amount of computation.

KW - signal processing

KW - least squares

KW - parameter estimation

KW - noise

KW - algorithms

KW - autoregressive processes

UR - http://handle.uws.edu.au:8081/1959.7/40936

M3 - Conference Paper

SN - 0780374886

BT - Proceedings of the 6th International Conference on Signal Processing

PB - IEEE Press

T2 - International Conference on Signal Processing

Y2 - 1 January 2010

ER -

A new estimation algorithm for AR signals measured in noise

Abstract

Conference

Keywords

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