A unified framework for bias compensation based methods in correlated noise case

Li Juan Jia; Ran Tao; Shunshoku Kanae; Zi Jiang Yang; Kiyoshi Wada

doi:10.1109/TAC.2010.2093250

A unified framework for bias compensation based methods in correlated noise case

Li Juan Jia^*, Ran Tao, Shunshoku Kanae, Zi Jiang Yang, Kiyoshi Wada

^*Corresponding author for this work

School of Information and Electronics

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

Abstract

This technical note presents a unified framework for bias compensation principle (BCP)-based methods applied for identification of linear systems subject to correlated noise. By introducing a non-singular matrix and an auxiliary vector uncorrelated with the noise, the unified framework is established. Since there are rich possibilities of the choices of the introduced matrix and vector, the proposed unified framework is very flexible. It can be verified that the existing BCP-based methods are special cases of the achieved result. It also shows that the unified framework can be used for deriving new or simplified versions of the BCP type methods.

Original language	English
Article number	5638608
Pages (from-to)	625-629
Number of pages	5
Journal	IEEE Transactions on Automatic Control
Volume	56
Issue number	3
DOIs	https://doi.org/10.1109/TAC.2010.2093250
Publication status	Published - Mar 2011

Keywords

Bias eliminated least-squares method
correlated noise
system identification
weighted instrumental variable method

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10.1109/TAC.2010.2093250

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abstract = "This technical note presents a unified framework for bias compensation principle (BCP)-based methods applied for identification of linear systems subject to correlated noise. By introducing a non-singular matrix and an auxiliary vector uncorrelated with the noise, the unified framework is established. Since there are rich possibilities of the choices of the introduced matrix and vector, the proposed unified framework is very flexible. It can be verified that the existing BCP-based methods are special cases of the achieved result. It also shows that the unified framework can be used for deriving new or simplified versions of the BCP type methods.",

keywords = "Bias eliminated least-squares method, correlated noise, system identification, weighted instrumental variable method",

author = "Jia, {Li Juan} and Ran Tao and Shunshoku Kanae and Yang, {Zi Jiang} and Kiyoshi Wada",

year = "2011",

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T1 - A unified framework for bias compensation based methods in correlated noise case

AU - Jia, Li Juan

AU - Tao, Ran

AU - Kanae, Shunshoku

AU - Yang, Zi Jiang

AU - Wada, Kiyoshi

PY - 2011/3

Y1 - 2011/3

N2 - This technical note presents a unified framework for bias compensation principle (BCP)-based methods applied for identification of linear systems subject to correlated noise. By introducing a non-singular matrix and an auxiliary vector uncorrelated with the noise, the unified framework is established. Since there are rich possibilities of the choices of the introduced matrix and vector, the proposed unified framework is very flexible. It can be verified that the existing BCP-based methods are special cases of the achieved result. It also shows that the unified framework can be used for deriving new or simplified versions of the BCP type methods.

AB - This technical note presents a unified framework for bias compensation principle (BCP)-based methods applied for identification of linear systems subject to correlated noise. By introducing a non-singular matrix and an auxiliary vector uncorrelated with the noise, the unified framework is established. Since there are rich possibilities of the choices of the introduced matrix and vector, the proposed unified framework is very flexible. It can be verified that the existing BCP-based methods are special cases of the achieved result. It also shows that the unified framework can be used for deriving new or simplified versions of the BCP type methods.

KW - Bias eliminated least-squares method

KW - correlated noise

KW - system identification

KW - weighted instrumental variable method

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M3 - Article

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EP - 629

JO - IEEE Transactions on Automatic Control

JF - IEEE Transactions on Automatic Control

IS - 3

M1 - 5638608

ER -

A unified framework for bias compensation based methods in correlated noise case

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Keywords

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