New results on FIR system identification using higher-order statistics

Author

Tugnait, Jitendra

Author_Institution

Dept. of Electr. Eng., Auburn Univ., AL, USA

fYear

1990

fDate

10-12 Oct. 1990

Firstpage

202

Lastpage

206

Abstract

The problem of estimating the parameters of a moving average model from the cumulant statistics of the noisy observations of the system output is considered. The system is driven by an i.i.d. (independent and identically distributed) non-Gaussian sequence that is not observed. The noise is additive and may be colored and non-Gaussian. Re-parametrization of an existing linear method, and a modification to it, are discussed. Simulation results show a distinct improvement in the numerical conditioning of both, the reparametrized algorithm and its modification, for the noisefree case. For the case of i.i.d. noise, the reparametrized algorithm shows a marked degradation in performance whereas its modification degrades far more gracefully.<>

Keywords

filtering and prediction theory; parameter estimation; statistical analysis; FIR system identification; additive noise; cumulant statistics; degradation; higher-order statistics; independently identically distributed nonGaussian sequence; moving average model; noisy observations; parameter estimation; reparametrized algorithm; system output; Additive noise; Degradation; Equations; Finite impulse response filter; Geophysical signal processing; Higher order statistics; Hydrogen; Parameter estimation; Statistical distributions; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Spectrum Estimation and Modeling, 1990., Fifth ASSP Workshop on

Conference_Location

Rochester, NY, USA

Type

conf

DOI

10.1109/SPECT.1990.205575

Filename

205575