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Abstract

The method of a phase shift angle measurement using conditional averaging of delayed signal absolute value (CAAV) is presented in this paper. The input sinusoidal signal x(t) is without noise. White noise with normal distribution and band limited to low frequencies has been applied as disturbance of delayed sinusoidal signal z(t). Noise n(t) - N(0, σn) is added to the delayed signal - the noised and delayed signal z(t) is obtained. The phase angle shift is proportional to time location of CAAV's minimum (minimum of the characteristic of conditional averaging of delayed signal's absolute value). The phase angle shift can be determined on the basis of conditional averaging value of elaborated algorithm. The characteristics of conditional average of delayed signal's absolute value in the surrounding of the minimum of this function (the results of practical investigations and theoretical calculation) are presented. The experimental variance of characteristic CAAV in surroundings of the minimum (obtained from practical investigations and calculation) is illustrated in the paper. The algorithms of conditional averaging have been elaborated and practically realized in the LabVIEW environment.
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Abstract

The correlation of data contained in a series of signal sample values makes the estimation of the statistical characteristics describing such a random sample difficult. The positive correlation of data increases the arithmetic mean variance in relation to the series of uncorrelated results. If the normalized autocorrelation function of the positively correlated observations and their variance are known, then the effect of the correlation can be taken into consideration in the estimation process computationally. A significant hindrance to the assessment of the estimation process appears when the autocorrelation function is unknown. This study describes an application of the conditional averaging of the positively correlated data with the Gaussian distribution for the assessment of the correlation of an observation series, and the determination of the standard uncertainty of the arithmetic mean. The method presented here can be particularly useful for high values of correlation (when the value of the normalized autocorrelation function is higher than 0.5), and for the number of data higher than 50. In the paper the results of theoretical research are presented, as well as those of the selected experiments of the processing and analysis of physical signals.
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