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Composite Vector Stochastic Processes Model in the Task of Signals' Recognition

Autoři: Chmelařová Natalija | Tykhonov Vyacheslav A.

Rok: 2016

Druh publikace: článek ve sborníku

Název zdroje: Radioelektronika 2016 : conference proceedings

Název nakladatele: IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání: New York

Strana od-do: 203-206

Tituly:

Jazyk	Název	Abstrakt	Klíčová slova
cze	Composite Vector Stochastic Processes Model in the Task of Signals' Recognition	The composite vector stochastic processes model is usable in many signal processing areas. Advantages of the model utilization, in task of electric motors acoustic signals parametric estimations, are shown in this paper. Models' results are compared with the traditional statistical methods for the signal analysis, in the two samples classes recognition task. The expressions for correlation function, autoregressive models' parameters calculation, and parametric power spectral density estimation in autoregressive composite vector stochastic processes representation, are shown in the paper. The proposed method for signals analysis, presented in this paper, enables to obtain information, which is difficult to gain by using traditional methods of statistical analysis.	Power spectrum density; Signal's recognition; Composite vector stochastic processes; Subvector; Correlation function; Autoregressive models
eng	Composite Vector Stochastic Processes Model in the Task of Signals' Recognition	The composite vector stochastic processes model is usable in many signal processing areas. Advantages of the model utilization, in task of electric motors acoustic signals parametric estimations, are shown in this paper. Models' results are compared with the traditional statistical methods for the signal analysis, in the two samples classes recognition task. The expressions for correlation function, autoregressive models' parameters calculation, and parametric power spectral density estimation in autoregressive composite vector stochastic processes representation, are shown in the paper. The proposed method for signals analysis, presented in this paper, enables to obtain information, which is difficult to gain by using traditional methods of statistical analysis.	Power spectrum density; Signal's recognition; Composite vector stochastic processes; Subvector; Correlation function; Autoregressive models

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