Selection of the order of autoregressive models for spectral analysis of noise corrupted signals

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25530%2F17%3A39921589" target="_blank" >RIV/00216275:25530/17:39921589 - isvavai.cz</a>
Result on the web
<a href="https://www.science-gate.com/IJAAS/V4I12/Jakub.html" target="_blank" >https://www.science-gate.com/IJAAS/V4I12/Jakub.html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21833/ijaas.2017.012.016" target="_blank" >10.21833/ijaas.2017.012.016</a>

Result language
angličtina
Original language name
Selection of the order of autoregressive models for spectral analysis of noise corrupted signals
Original language description
This paper presents the theoretical basis of autoregressive (AR) modelling in spectral analysis. Autoregressive modelling includes a model identification procedure based on an autocorrelation function (ACF) of the incoming signal and its statistical evaluation. This is necessary to choose the best order of an AR model that best describes the given set of data. Spectral analysis gives information about the frequency content of a signal. The AR method is an alternative to discrete Fourier transform (DFT) in the computing of a power spectrum density function of a signal, but provides a smoother power spectral density then DFT. (C) 2017 The Authors. Published by IASE.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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