Optimum segmentation and windowing in nonparametric power spectral density estimation

被引：0

作者：

Beheshti, Soosan ^{[1
]}

Pal, Sudeshna ^{[1
]}

机构：

[1] Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON, Canada

来源：

PROCEEDINGS OF THE 2007 15TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING | 2007年

关键词：

autocorrelation; periodogram; spectral analysis; estimation;

D O I：

10.1109/ICDSP.2007.4288598

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In averaging power spectral density (PSD) estimation methods, such as Bartlett and Welch approaches, segmented version of data is used. However. no systematic method for the choice of optimum segmentation is available. In this paper, we provide a novel approach to nonparametric PSD estimation that not only provides the optimum segmentation in these approaches, but also combines these approaches with a new optimum windowing within the segments. The desired criterion in this method is PSD mean square error that is estimated for windows and segments of different length. The new optimum windowing approach outperforms the existing nonparametric approaches.

引用

页码：379 / +

页数：2

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