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The paper develops a theory of a new so-called two-parametric approach to the random signals' analysis and processing. A mathematical simulation and the task solutions’ comparison have been implemented for the Gauss and Rice statistical models. The applicability of the Rice statistical model is substantiated for the tasks of data and images processing when the signal’s envelope is being analyzed. A technique is developed and theoretically substantiated for solving the task of the noise suppression and initial image reconstruction by means of joint calculation of both statistical parameters — an initial signal’s mean value and noise dispersion — based on the maximum likelihood method within the Rice distribution. The peculiarities of this distribution’s likelihood function and the following from them possibilities of the signal and noise estimation have been analyzed.

The paper provides a solution of the two-parameter task of joint signal and noise estimation at data analysis within the conditions of the Rice distribution by the techniques of mathematical statistics: the maximum likelihood method and the variants of the method of moments. The considered variants of the method of moments include the following techniques: the joint signal and noise estimation on the basis of measuring the 2-nd and the 4-th moments (MM24) and on the basis of measuring the 1-st and the 2-nd moments (MM12). For each of the elaborated methods the explicit equations’ systems have been obtained for required parameters of the signal and noise. An important mathematical result of the investigation consists in the fact that the solution of the system of two nonlinear equations with two variables — the sought for signal and noise parameters — has been reduced to the solution of just one equation with one unknown quantity what is important from the view point of both the theoretical investigation of the proposed technique and its practical application, providing the possibility of essential decreasing the calculating resources required for the technique’s realization. The implemented theoretical analysis has resulted in an important practical conclusion: solving the two-parameter task does not lead to the increase of required numerical resources if compared with the one-parameter approximation. The task is meaningful for the purposes of the rician data processing, in particular — the image processing in the systems of magnetic-resonance visualization. The theoretical conclusions have been confirmed by the results of the numerical experiment.

The paper presents the results of the fundamental research directed on the theoretical study and computer simulation of peculiarities of the quasi-harmonic signal’s phase statistical distribution. The quasi-harmonic signal is known to be formed as a result of the Gaussian noise impact on the initially harmonic signal. By means of the mathematical analysis the formulas have been obtained in explicit form for the principle characteristics of this distribution, namely: for the cumulative distribution function, the probability density function, the likelihood function. As a result of the conducted computer simulation the dependencies of these functions on the phase distribution parameters have been analyzed. The paper elaborates the methods of estimating the phase distribution parameters which contain the information about the initial, undistorted signal. It has been substantiated that the task of estimating the initial value of the phase of quasi-harmonic signal can be efficiently solved by averaging the results of the sampled measurements. As for solving the task of estimating the second parameter of the phase distribution, namely — the parameter, determining the signal level respectively the noise level — a maximum likelihood technique is proposed to be applied. The graphical illustrations are presented that have been obtained by means of the computer simulation of the principle characteristics of the phase distribution under the study. The existence and uniqueness of the likelihood function’s maximum allow substantiating the possibility and the efficiency of solving the task of estimating signal’s level relative to noise level by means of the maximum likelihood technique. The elaborated method of estimating the un-noised signal’s level relative to noise, i. e. the parameter characterizing the signal’s intensity on the basis of measurements of the signal’s phase is an original and principally new technique which opens perspectives of usage of the phase measurements as a tool of the stochastic data analysis. The presented investigation is meaningful for solving the task of determining the phase and the signal’s level by means of the statistical processing of the sampled phase measurements. The proposed methods of the estimation of the phase distribution’s parameters can be used at solving various scientific and technological tasks, in particular, in such areas as radio-physics, optics, radiolocation, radio-navigation, metrology.

The article describes the examples of the analysis of the experimental data spectra for identifying typical structures of processes forming plasma turbulence. The method is based on the original algorithm which is close to the one-sample bootstrap. The base model for description of the fine structure of stochastic processes is finite local-scale normal mixtures. For finding the statistical estimates (maximum likelihood estimates) well known EM algorithm is used. The efficiency of the proposed research technique is demonstrated for a number of spectra’s set obtained in different modes of low-frequency plasma turbulence.

The paper develops a new mathematical method of the joint signal and noise calculation at the Rice statistical distribution based on combing the maximum likelihood method and the method of moments. The calculation of the sough-for values of signal and noise is implemented by processing the sampled measurements of the analyzed Rician signal’s amplitude. The explicit equations’ system has been obtained for required signal and noise parameters and the results of its numerical solution are provided confirming the efficiency of the proposed technique. It has been shown that solving the two-parameter task by means of the proposed technique does not lead to the increase of the volume of demanded calculative resources if compared with solving the task in one-parameter approximation. An analytical solution of the task has been obtained for the particular case of small value of the signal-to-noise ratio. The paper presents the investigation of the dependence of the sought for parameters estimation accuracy and dispersion on the quantity of measurements in experimental sample. According to the results of numerical experiments, the dispersion values of the estimated sought-for signal and noise parameters calculated by means of the proposed technique change in inverse proportion to the quantity of measurements in a sample. There has been implemented a comparison of the accuracy of the soughtfor Rician parameters’ estimation by means of the proposed technique and by earlier developed version of the method of moments. The problem having been considered in the paper is meaningful for the purposes of Rician data processing, in particular, at the systems of magnetic-resonance visualization, in devices of ultrasonic visualization, at optical signals’ analysis in range-measuring systems, at radar signals’ analysis, as well as at solving many other scientific and applied tasks that are adequately described by the Rice statistical model.