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Composite Vector Stochastic Processes Model in the Task of Signals' Recognition
Authors: Chmelařová Natalija | Tykhonov Vyacheslav A.
Year: 2016
Type of publication: článek ve sborníku
Name of source: Radioelektronika 2016 : conference proceedings
Publisher name: IEEE (Institute of Electrical and Electronics Engineers)
Place: New York
Page from-to: 203-206
Titles:
Language Name Abstract Keywords
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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