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FIR modelling for errors‐in‐variables/closed‐loop systems by exploiting cyclo‐stationarity
Author(s) -
Wang Jiandong,
Chen Tongwen,
Huang Biao
Publication year - 2007
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.948
Subject(s) - impulse response , parametric statistics , impulse (physics) , computer science , class (philosophy) , finite impulse response , parametric model , control theory (sociology) , mathematics , algorithm , statistics , artificial intelligence , mathematical analysis , physics , control (management) , quantum mechanics
Finite impulse response (FIR) modelling of errors‐in‐variables/closed‐loop systems by correlation analysis usually yields biased estimates due to the additive noises on inputs and outputs. A non‐parametric approach, the cyclic correlation analysis (CCRA), provides asymptotically unbiased and consistent estimates. The main feature of the CCRA is to eliminate the adverse effects of stationary noises by exploiting cyclo‐stationarity that may exist naturally or be induced artificially. A complete study of the CCRA is developed, including the statistical performance of the estimated FIR model. Frequency‐domain expressions of the statistical performance provide guidelines in designing a class of cyclo‐stationary signals for modelling. Effectiveness and properties of the CCRA are validated and illustrated by numerical examples. Copyright © 2007 John Wiley & Sons, Ltd.