This email address is being protected from spambots. You need JavaScript enabled to view it.
 
+7 (4912) 72-03-73
 
Интернет-портал РГРТУ: http://rsreu.ru

UDC 621.37:51-74

PARAMETRIC SPECTRAL ANALYSIS WITH GAUSSIAN SHAPE UNIMODAL SPECTRUM FOR NOISY SIGNALS

V. G. Andrejev, PhD (technical sciences), professor, RSREU, Ryazan, This email address is being protected from spambots. You need JavaScript enabled to view it.
H. L. Tran, post-graduate student, RSREU, Ryazan, This email address is being protected from spambots. You need JavaScript enabled to view it.

We proposed and investigated a method of restoring autocorrelation coefficients of discrete autocorrelation function of random signals with Gaussian shape of power spectral density for the construction of parametric models. The aim of the work is to develop methods of improving the accuracy of spectral estimation of known signals in the shape of their spectral mode. The method is based on finding the optimum value of the width ΔFT of unimodal spectrum in Gaussian shape. Finding ΔFT gives an opportunity to restore distorted noise autocorrelation coefficients of a random process to improve the quality of its spectral estimation. Experimental research has shown that the proposed method makes it possible to reduce 1,4...6 times the discrepancy between the control and the measured spectrums in comparison with the known approaches to parametric spectral analysis, in particular, AR method. Increasing the adequacy of spectral estimation makes it possible to reduce in 3...6 times the length of analyzed time sample while maintaining achievable by other known methods of parametric analysis accuracy of spectral analysis. Winnings are achieved through the use of a priori information about the spectral properties of the process.

Key words: spectrum, spectral estimation, restoration of autocorrelation coefficients, autoregressive model, autoregression, power spectral density.

 Скачать статью