The Shannon entropy metric modified for solving the problem of estimating the information capabilities of adaptive radio engineering system in conditions of intrasystem uncertainty has been considered. The application of entropy approach was shown as a tool of the generalized representation of known criteria of adaptive signal processing during the intrasystem perturbations of system parametric vector.

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Одесская государственная академия технического регулирования и качества, Одесса, Украина Background. The task of informative parameters of radio engineering signal measurement in the case of domination in target result error the component that generated by additive noises of an external environment of measurement is characteristic for many applications of radio engineering, monitoring of the environment, monitoring and technical diagnostics. The optimal solution of "weight's" minimization of the dominating error in mean squared sense as a result of measurement for the purpose of its approximation to the reference is considered. Objective. The aim of the paper is the application of preprocessing with the adaptive filtering of additive noises in the task of minimization of the dominating measurement error of informative parameters of "noisy" sampling of a radio engineering signal. Methods. Scientific novelty of work consists in modification of the form of the operator measurement's equation of information parameters considering specifics of procedure of bleaching. The problem of preprocessing of observed selection for automatic matching of measurement's procedure with input signal parameters and receiving the maximum number of information on an input of measuring instrument is solved. Efficiency of such preprocessing is estimated by information criterion which generalized measure is the modified entropy metrics according to Shannon. Results. Problem situations in informative parameters measurement tasks of a radio engineering signal against additive noises of arbitrary intensity are considered. Operator and algorithmic forms of the modified measurement equation of signal's information parameters, and also option of their implementation in the form of the device of the preprocessing with the adaptive filtering performed according to the diagram of the linear weight summing are provided. Efficiency of the adaptive filtering "noisy" sampling of a measuring signal is estimated and validity of operation of preprocessing which allows minimizing information losses on measurement instrument input is confirmed. Conclusions. Features of the measuring task solution are investigated and propositions on application of the preprocessing technology with the adaptive filtering additive noises of an external environment for minimization of "weight" of the dominating error in the result of measurement are formulated.

The problem of forming sample estimates of the correlation matrix of observations that satisfy the criterion "computational stability – consistency" is considered. The variants in which the direct and inverse asymptotic forms of the correlation matrix of observations are approximated by various types of estimates formed from a sample of a fixed volume are investigated. The consistency of computationally stable estimates of the correlation matrix for their static regularization was analyzed. The contradiction inherent in the problem of regularization of the estimates with a fixed parameter is revealed. The dynamic regularization method as an alternative approach is proposed, which is based on the uniqueness theorem for solving the inverse problem with perturbed initial data. An optimal mean-square approximation algorithm has been developed for dynamic regularization of sample estimates of the correlation matrix of observations, using the law of monotonic decrease in the regularizing parameter with increasing sample size. An optimal dynamic regularization function was obtained for sample estimates of the correlation matrix under conditions of a priori uncertainty with respect to their spectral composition. The preference of this approach to the regularization of sample estimates of the correlation matrix under conditions of a priori uncertainty is proved, which allows to exclude the domain of computational instability from solving the inverse problem and obtain its solution in real time without involving prediction data and additional computational cost for finding the optimal value of the regularization parameter. The application of the dynamic regularization method is shown for solving the problem of detecting a signal at the output of an adaptive antenna array in a nondeterministic clutter and jamming environment. The results of a computational experiment that confirm the main conclusions are presented.

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