PARAMETRIC SPECTRAL ANALYSIS OF PIECEWISE-STATIONARY RADIOENGINEERING SIGNALS
We proposed and investigated a method for the spectral analysis of processes when they are observed on the background of additive piecewise-stationary noise. The aim of the work is to increase computational efficiency of analysis algorithms and the accuracy of radio engineering signal spectral estimation on the background of piecewise-stationary noises. Based on the estimation of weight coefficient w<sub>2</sub> optimal value the method proposed makes it possible to take into account fragments of a piecewise-stationary random process distorted by noise in order to reduce the influence of non-stationary noise and improve the accuracy of spectral estimates. The qualitative indicators of modified spectral analysis method proposed are compared with conventional autoregression method. Experimental studies have shown that the method proposed makes it possible to reduce the discrepancy between control and estimated spectra by 4.1...6.9 times in comparison with the known methods of spectral analysis, in particular with the autoregressive method. When comparing efficiency with conventional autoregressive model, the decrease in the order of p can reach 2…2.5 times. It is confirmed that to analyze the spectrum of narrowband radioengineering signals studied, relative deviations ΔF of the estimate of dominant frequency are significantly (up to 5 times) reduced by using the proposed modified method in comparison with autoregressive method. Winnings are achieved through the use of a priori information about time-varying power of interfering process.