The use of wavelets for spectral density estimation with local bandwidth adaptation

Author

Moulin, Pierre

Author_Institution

Bell Commun. Res., Morristown, NJ, USA

fYear

1994

fDate

27 Jun-1 Jul 1994

Firstpage

40

Abstract

We consider the problem of estimating the spectral density of a discrete-time, wide-sense stationary, real, Gaussian random process from a set of 2N observations. Consistent estimates may be obtained by suitable processing of the empirical spectral density estimates (periodogram). Wavelet techniques can be used for combining information about the spectral density at different resolutions. We present an estimation technique based on the following two paradigms: large-sample model for the data; and inference on the wavelet coefficients of the log spectral density

Keywords

Gaussian processes; adaptive signal processing; random processes; signal resolution; signal sampling; spectral analysis; wavelet transforms; Gaussian random process; discrete-time stationary process; large-sample model; local bandwidth adaptation; log spectral density; periodogram; resolutions; spectral density estimation; wavelet coefficients; wavelets; Additive noise; Additive white noise; Bandwidth; Discrete wavelet transforms; Random processes; Random variables; Smoothing methods; Testing; Wavelet domain; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 1994. Proceedings., 1994 IEEE International Symposium on

Conference_Location

Trondheim

Print_ISBN

0-7803-2015-8

Type

conf

DOI

10.1109/ISIT.1994.394931

Filename

394931