Unbiased RLS identification of errors-in-variables models in the presence of correlated noise

We propose an unbiased recursive-least-squares(RLS)-type algorithm for errors-in-variables system identification when the input noise is colored and correlated with the output noise. To derive the proposed algorithm, which we call unbiased RLS (URLS), we formulate an exponentially-weighted least-squ...

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Bibliographic Details
Published in2014 22nd European Signal Processing Conference (EUSIPCO) pp. 261 - 265
Main Authors Arablouei, Reza, Dogancay, Kutluyil, Adali, Tulay
Format Conference Proceeding
LanguageEnglish
Published EURASIP 01.09.2014
Subjects
Abstracts
Adaptive estimation
Complexity theory
dichotomous coordinate-descent algorithm
errors-in-variables modeling
Field programmable gate arrays
Indexes
Noise
recursive least-squares
system identification
Uniform resource locators
Vectors
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ISSN2219-5491
2219-5491

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Summary:We propose an unbiased recursive-least-squares(RLS)-type algorithm for errors-in-variables system identification when the input noise is colored and correlated with the output noise. To derive the proposed algorithm, which we call unbiased RLS (URLS), we formulate an exponentially-weighted least-squares problem that yields an unbiased estimate. Then, we solve the associated normal equations utilizing the dichotomous coordinate-descent iterations. Simulation results show that the estimation performance of the proposed URLS algorithm is similar to that of a previously proposed bias-compensated RLS (BCRLS) algorithm. However, the URLS algorithm has appreciably lower computational complexity as well as improved numerical stability compared with the BCRLS algorithm.
ISSN:2219-5491
2219-5491