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UDC 621.37:51-74

PARAMETRIC SPECTRAL ANALYSIS FOR NOISY SIGNALS WITH GAUSSIAN SPECTRUM

V. G. Andreev, 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.
V. A. Belokurov, PhD, associate professor, 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 for unimodal random signal spectrum to build their autoregression models. The aim is to develop a compensation method of additive white Gaussian noise on measured coefficients of process autocorrelation to improve the accuracy of spectral estimation of useful signal with Gaussian shape of spectrum. The method is based on solving the system of equations which is used between measured dependence of autocorrelation coefficients when exposed to additive white Gaussian noise. Experiments have shown that the approach proposed makes it possible to reduce 1.5 ... 2.5 times the discrepancy between control and model spectrum as compared to the known methods of spectral analysis, in particular MUSIC.

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

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