Fast convergent algorithm for identification of noisy autoregressive signals

Wei Xing Zheng

doi:10.1109/ISCAS.2000.858797

Fast convergent algorithm for identification of noisy autoregressive signals

Wei Xing Zheng

School of Computer, Data & Mathematical Sciences

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

A fast convergent algorithm for unbiased identification of noisy autoregressive (AR) signals is presented. This algorithm is developed based on a bias correction procedure, but makes use of more autocovariances to estimate the variance of the corrupting noise which determines the noise-induced bias in the least-squares estimates of the AR parameters. Since better estimates of this corrupting noise variance can be attained at earlier stages of the iterative process, the proposed algorithm can achieve a faster rate of convergence. Simulation results are included that illustrate the good performances of the proposed algorithm.

Original language	English
Pages (from-to)	IV-497-IV-500
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	4
DOIs	https://doi.org/10.1109/ISCAS.2000.858797
Publication status	Published - 2000
Event	Proceedings of the IEEE 2000 International Symposium on Circuits and Systems, ISCAS 2000 - Geneva, Switz, Switzerland Duration: 28 May 2000 → 31 May 2000

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10.1109/ISCAS.2000.858797

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title = "Fast convergent algorithm for identification of noisy autoregressive signals",

abstract = "A fast convergent algorithm for unbiased identification of noisy autoregressive (AR) signals is presented. This algorithm is developed based on a bias correction procedure, but makes use of more autocovariances to estimate the variance of the corrupting noise which determines the noise-induced bias in the least-squares estimates of the AR parameters. Since better estimates of this corrupting noise variance can be attained at earlier stages of the iterative process, the proposed algorithm can achieve a faster rate of convergence. Simulation results are included that illustrate the good performances of the proposed algorithm.",

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year = "2000",

doi = "10.1109/ISCAS.2000.858797",

language = "English",

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

issn = "0271-4310",

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note = "Proceedings of the IEEE 2000 International Symposium on Circuits and Systems, ISCAS 2000 ; Conference date: 28-05-2000 Through 31-05-2000",

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PY - 2000

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N2 - A fast convergent algorithm for unbiased identification of noisy autoregressive (AR) signals is presented. This algorithm is developed based on a bias correction procedure, but makes use of more autocovariances to estimate the variance of the corrupting noise which determines the noise-induced bias in the least-squares estimates of the AR parameters. Since better estimates of this corrupting noise variance can be attained at earlier stages of the iterative process, the proposed algorithm can achieve a faster rate of convergence. Simulation results are included that illustrate the good performances of the proposed algorithm.

AB - A fast convergent algorithm for unbiased identification of noisy autoregressive (AR) signals is presented. This algorithm is developed based on a bias correction procedure, but makes use of more autocovariances to estimate the variance of the corrupting noise which determines the noise-induced bias in the least-squares estimates of the AR parameters. Since better estimates of this corrupting noise variance can be attained at earlier stages of the iterative process, the proposed algorithm can achieve a faster rate of convergence. Simulation results are included that illustrate the good performances of the proposed algorithm.

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DO - 10.1109/ISCAS.2000.858797

M3 - Conference article

AN - SCOPUS:18544404074

SN - 0271-4310

VL - 4

SP - IV-497-IV-500

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

T2 - Proceedings of the IEEE 2000 International Symposium on Circuits and Systems, ISCAS 2000

Y2 - 28 May 2000 through 31 May 2000

ER -

Fast convergent algorithm for identification of noisy autoregressive signals

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