Analysis of the maximum likelihood, total least squares and principal component approaches for frequency response function estimation
Analysis of the maximum likelihood, total least squares and principal component approaches for frequency response function estimation
This paper considers the problem of estimation frequency response functions (FRFs) for a single-input single-output (SISO) system in the presence of additive noise on both input and output measurements. It demonstrates that principle component analysis (PCA) can be employed to solve such problems and demonstrates that this is equivalent to the methods based on total least squares (TLS). FRF estimation is also cast as a problem in statistical inference and the use of the principle of maximum likelihood (ML) leads to a novel development of a generalised TLS scheme. This analysis also provides a framework within which one can compute asymptotic expressions for the variance of such estimators.
676-689
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Tan, M.H.
4d02e6ad-7915-491c-99cc-a1c85348267c
Hammond, J.K.
9ee35228-a62c-4113-8394-1b24df97b401
7 March 2006
White, P.R.
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Tan, M.H.
4d02e6ad-7915-491c-99cc-a1c85348267c
Hammond, J.K.
9ee35228-a62c-4113-8394-1b24df97b401
White, P.R., Tan, M.H. and Hammond, J.K.
(2006)
Analysis of the maximum likelihood, total least squares and principal component approaches for frequency response function estimation.
Journal of Sound and Vibration, 290 (3-5), .
(doi:10.1016/j.jsv.2005.04.029).
Abstract
This paper considers the problem of estimation frequency response functions (FRFs) for a single-input single-output (SISO) system in the presence of additive noise on both input and output measurements. It demonstrates that principle component analysis (PCA) can be employed to solve such problems and demonstrates that this is equivalent to the methods based on total least squares (TLS). FRF estimation is also cast as a problem in statistical inference and the use of the principle of maximum likelihood (ML) leads to a novel development of a generalised TLS scheme. This analysis also provides a framework within which one can compute asymptotic expressions for the variance of such estimators.
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Published date: 7 March 2006
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Local EPrints ID: 28365
URI: http://eprints.soton.ac.uk/id/eprint/28365
ISSN: 0022-460X
PURE UUID: fc6981cf-658b-4386-96cd-57a2a1229035
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Date deposited: 28 Apr 2006
Last modified: 11 Jul 2024 01:33
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Author:
J.K. Hammond
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