MODELS OF BIAS OF MEAN SQUARE VALUE DIGITAL ESTIMATOR FOR SELECTED DETERMINISTIC AND RANDOM SIGNALS

摘要

The article presents the probability density functions as well as characteristic functions of selected periodic and random signals. On their basis, original models of the bias of the mean square value digital estimator have been designed. These models were employed in investigating estimation errors caused by analog to digital conversion and analog to digital conversion with a dither signal. Selected graphical model representations and their analyses are demonstrated. It has been shown that for a triangular probability density function random signal with the amplitude A_(t) velence kq, kEN{0}, mean square value reconstruction occurs on the basis of a signal quantized with the accuracy of Sheppard's correction. Whereas for periodic signals as well as for the sum of periodic and random signals, the delta component of bias due to the nonsatisfaction of the reconstruction condition is a suppressed oscillating function of the quotient of the amplitude A and the quantization step size q. It has been proved that by adding, prior to quantization, a triangular distribution random signal with zero mean and the amplitude A_(t) velence kq (k velence 1, 2, ...) in the mean square value measurement of any periodic signal, this bias component can be brought to zero.