Adaptive filtering via cumulants and LMS algorithm

Author

Chiang, Hsing-Hsing ; Nikias, Chrysostomos L.

Author_Institution

Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA

fYear

1988

fDate

11-14 Apr 1988

Firstpage

1479

Abstract

A novel adaptive identification scheme is introduced for a nonGaussian white-noise-driven linear, nonminimum-phase FIR (finite-impulse response) system. The adaptive scheme is based on noncausal autoregressive (AR) modeling of higher-order cumulants of the system output. In particular, the magnitude and phase response estimates at each iteration are expressed in terms of the updated parameters of the noncausal AR model. The set of updated AR parameters is obtained by using the LMS (least-mean-squares) algorithm and by using higher-order cumulants instead of time samples of the output signal. It is demonstrated by means of standard examples that the new adaptive scheme works well and, as expected, outperforms the modified (autocorrelation-based) LMS algorithm for nonminimum-phase system identification

Keywords

digital filters; filtering and prediction theory; least squares approximations; signal processing; FIR filter; LMS algorithm; adaptive filtering; digital filter; finite-impulse response; higher-order cumulants; least-mean-squares; magnitude response; noncausal autoregressive modelling; nonminimum-phase system identification; phase response; signal processing; Adaptive filters; Adaptive systems; Autocorrelation; Equations; Filtering algorithms; Finite impulse response filter; Least squares approximation; Phase estimation; Silicon carbide;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on

Conference_Location

New York, NY

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1988.196882

Filename

196882