(Almost) periodic moving average system identification using higher order cyclic-statistics

Author

Liang, Ying-Chang ; Leyman, A. Rahim

Author_Institution

Dept. of Electr. Eng., Maryland Univ., College Park, MD, USA

Volume

46

Issue

3

fYear

1998

fDate

3/1/1998 12:00:00 AM

Firstpage

779

Lastpage

783

Abstract

This article addresses the problem of (almost) periodic moving average (APMA) system identification. Two new normal equations relating the coefficients of an APMA system and the time-varying higher order cumulants of the measurements are established, from which two new linear algebraic algorithms are presented for system parameter estimation. In addition, a new singular value decomposition (SVD) based algorithm is proposed for determining the system order. Simulation examples are given to demonstrate the performance of these new approaches

Keywords

higher order statistics; linear algebra; moving average processes; parameter estimation; signal processing; singular value decomposition; time-varying systems; APMA system coefficients; SVD based algorithm; higher order cyclic-statistics; linear algebraic algorithms; measurements; normal equations; performance; periodic moving average system identification; signal processing; simulation; singular value decomposition; system order; system parameter estimation; time-varying higher order cumulants; Equations; Higher order statistics; Hydrology; Meteorology; Noise measurement; Parameter estimation; Singular value decomposition; System identification; Time varying systems; Wireless communication;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.661346

Filename

661346