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Discrete quasi-optimal demodulation of signals in correlated non-Gaussian multiplicative fluctuation noise

Keywords:

V.M. Artyushenko – Dr. Sc. (Eng.), Professor, Head of Department of Information Technology and Management Systems, Technological University (Korolyov, Moscow region). E-mail: artuschenko@mail.ru V.I. Volovach – Ph. D. (Eng.), Associate Professor, Head of Department of Information and Electronic Service, Volga Region State University of Service (Togliatti). E-mail: volovach.vi@mail.ru


The article considers the issues of discrete quasi-optimal demodulation of signals in correlated non-Gaussian multiplicative fluctuation noise. We obtained the recurrent equations describing the quasi-optimal algorithm for estimating the correlated multiplicative noise with an arbitrary probability density function (PDF), the components of which describe the structure of the block of nonlinear transformations of the signal and the structure of the block of nonlinear transformation of the envelope (amplitude) of the processed signal. We obtained the structure diagram of the demodulator constructed using the above mentioned recurrent equations. This diagram consists of two channels: the channel of evaluation formation of the demodulated parameter and the channel of gain formation. It is shown that the above diagram models the estimation formation of the demodulation information parameter and it is a nonlinear non-stationary discrete tracking system. Thus the estimation of the information parameter in the current step is formed on the basis of extrapolated estimates formed by the extrapolation block. It is noted that the synthesized algorithms for the estimation of the information process include characteristics of the nonlinear transformation of the signal amplitude, which, in turn, are determined by the PDF of the multiplicative noise. It is shown that the optimal processing efficiency of the information process in correlated multiplicative noise, the PDF of which in general case differs from Gaussian, and completely determined by Fisher information matrices. In this case stationary error of demodulation (filtering) of the information process depends on the Fisher variance information. It is shown that accounting for correlation properties of multiplicative noise can significantly improve the accuracy of estimation of an information component.
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