Adaptive windowing in nonparametric power spectral density estimation

Author

Beheshti, Soosan ; Ravan, Maryam

Author_Institution

Electr. & Comput. Eng. Dept., Ryerson Univ., Toronto, ON

fYear

2008

fDate

4-7 May 2008

Abstract

In this paper we consider nonparametric power spectral density (PSD) estimation. We use finite length observed data not only to estimate the PSD by one of the existing periodogram estimators, but also to estimate the mean square error (MSE) in PSD estimation. Minimizing this criterion provides the optimum window length for the Blackman-Tukey approach. It can also provide extra adaptive window for PSD estimates of periodogram averaging methods such as Bartlett and Welch approaches. We demonstrate that the new additional optimum windowing improves the performance of the existing averaging periodogram approaches.

Keywords

estimation theory; mean square error methods; spectral analysis; Blackman-Tukey approach; MSE; adaptive windowing; mean square error; nonparametric power spectral density estimation; periodogram averaging methods; Autocorrelation; Convolution; Estimation error; Filters; Gaussian processes; Mean square error methods; Power engineering and energy; Power engineering computing; Random processes; Spectral analysis; Correlation; estimation; periodogram; spectral analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on

Conference_Location

Niagara Falls, ON

ISSN

0840-7789

Print_ISBN

978-1-4244-1642-4

Electronic_ISBN

0840-7789

Type

conf

DOI

10.1109/CCECE.2008.4564725

Filename

4564725