Skip to main content
Article
Using variability related to families of spectral estimators for mixed random processes
Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME
  • Li Wen, Iowa State University
  • Changxue Wang, Iowa State University
  • Peter J. Sherman, Iowa State University
Document Type
Article
Disciplines
Publication Version
Published Version
Publication Date
12-1-2001
DOI
10.1115/1.1409257
Abstract

Traditionally, characterization of spectral information for wide sense stationary processes has been addressed by identifying a single best spectral estimator from a given family. If one were to observe significant variability in neighboring spectral estimators then the level of confidence in the chosen estimator would naturally be lessened. Such variability naturally occurs in the case of a mixed random process, since the influence of the point spectrum in a spectral density characterization arises in the form of approximations of Dirac delta functions. In this work we investigate the nature of the variability of the point spectrum related to three families of spectral estimators: Fourier transform of the truncated unbiased correlation estimator, the truncated periodogram, and the autoregressive estimator. We show that tones are a significant source of bias and variability. This is done in the context of Dirichlet and Fejer kernels, and with respect to order rates. We offer some expressions for estimating statistical and arithmetic variability. Finally, we include an example concerning helicopter vibration. These results are especially pertinent to mechanical systems settings wherein harmonic content is prevalent.

Comments

This article is from Journal of Dynamic Systems, Measurement and Control 123 (2001): 572, doi: 10.1115/1.1409257. Posted with permission.

Copyright Owner
ASME
Language
en
Date Available
2015-09-01
File Format
application/pdf
Citation Information
Li Wen, Changxue Wang and Peter J. Sherman. "Using variability related to families of spectral estimators for mixed random processes" Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME Vol. 123 Iss. 4 (2001) p. 572 - 584
Available at: http://works.bepress.com/peter_sherman/1/