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Cumulant‐based approach to FIR system identification
Author(s) -
AlSmadi Adnan
Publication year - 2003
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.254
Subject(s) - realization (probability) , cumulant , gaussian , gaussian noise , mathematics , algorithm , higher order statistics , system identification , noise (video) , signal (programming language) , sequence (biology) , computer science , signal processing , statistics , artificial intelligence , data modeling , digital signal processing , physics , quantum mechanics , database , biology , computer hardware , image (mathematics) , genetics , programming language
In this paper, a non‐recursive approach is developed for estimating the coefficients of a moving average (MA) model from only third‐order cumulant statistics of a finite realization of the observations of the output data. The signal observations may be noisy. The excitation signal is assumed to be zero mean, non‐Gaussian stationary sequence that is not observed. The noise is additive and may be coloured Gaussian or non‐Gaussian. This novel technique is based on forming a third‐order cumulant composite data matrix. The method presented here requires the solution of a system of linear equations, which can be achieved using least‐squares methods. The proposed approach is illustrated via computer simulations and is shown to be consistent. Copyright © 2003 John Wiley & Sons, Ltd.