Алгоритм оцінювання частоти комплексного гармонічного сигналу з сегментацією даних спостережень

The article deals with the technology of estimating the frequency of harmonic components in the presence of additive normal interferences for solving applied problems of spectral analysis. Objective: to develop a methodology for the synthesis of algorithms for determining the frequency of a complex harmonic signal in discrete sections of the process, this is observed when using data segmentation. Objective: to develop the optimal technology for determining the frequency of the hormonal component of the process, provided by a finite number of discrete compartments, according to model representations and requirements that meet the problems of the current state of spectral analysis practice. These results were obtained. The problem of estimating the harmonic frequency from segmented data in the presence of additive Gaussian interference in observations based on the method of maximum likelihood is solved. The processing algorithm and the consequences of digital modeling of the synthesized evaluation technology for a given number of discrete process samples are given. The analysis of both the practical capacity of the technology for determining the assessment and certain qualitative indicators of assessment is performed. Conclusions. The scientific novelty of the obtained results is as follows: further development as a method for solving problems of estimating the frequency of the harmonic signal from a few sample values of the process under conditions of additive normal interference and methods for optimizing the structure of digital processing of observations in data segmentation. The synthesized technology uses one sample of observations to determine the estimates, which ensure the efficiency of information processing in a simple software implementation. The use of segmentation in the technological process of digital processing of observations allows obtaining estimates, the quality of which corresponds to the indicators of maximum likelihood. For unambiguous assessment, there is a need to eliminate ambiguity. Under these conditions, the technology with a given number of samples can significantly solve the range of signal-to-noise ratios at which can be obtained unbiased estimates.