A method for identifying non-Gaussian parametric model with time-varying coefficients

Author

Shen, Minfen ; Song, Rong ; Ting, K.H. ; Chan, Francis H Y

Author_Institution

Sci. Res. Center, Shantou Univ., Guangdong Shantou, China

Volume

6

fYear

2003

fDate

6-10 April 2003

Abstract

A method for identifying a non-Gaussian AR model with time-varying parameters is addressed. The proposed approach is based on the application of higher-order spectra (HOS) and wavelet analysis. To solve the problem and identify the characteristics of the time-varying linear system, a time-varying parametric model is proposed as a non-Gaussian AR model. The model coefficients that characterize the time-varying system are the functions of time and can be represented by a family of wavelet basis functions, having the invariant basis coefficients. This method can well track the changes of the model coefficients. The experimental results show the effectiveness of the proposed approach.

Keywords

autoregressive processes; higher order statistics; parameter estimation; spectral analysis; time-varying systems; wavelet transforms; higher-order spectra; identification; invariant basis coefficients; nonGaussian AR model; nonGaussian parametric model; time-varying coefficients; time-varying linear system; wavelet analysis; wavelet basis functions; Difference equations; Gaussian processes; Linear systems; Parameter estimation; Parametric statistics; Signal processing; System identification; TV; Time varying systems; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on

ISSN

1520-6149

Print_ISBN

0-7803-7663-3

Type

conf

DOI

10.1109/ICASSP.2003.1201760

Filename

1201760