Author: A. V. Akimov, A. A. Sirota (Voronezh State University, Voronezh, Russia)
Radioelectronics and Communications Systems , Volume 60, Issue 10, pp 458–468 (October 2017)
DOI: https://doi.org/10.3103/S0735272717100041
Abstract
The problem of digital signal recognition has been considered in conditions of deforming distortions of the waveform of these signals and additive Gaussian noise. A mathematical model for introducing deformations of the known or random waveform signals is proposed for synthesizing recognition algorithms. The model is based on introducing the nonlinear deformation operator as an operator of permutations with repetitions of elements of the initial discrete signal with addition of additive noise component caused by quantization errors of continuous deformation function. Two recognition algorithms were synthesized and investigated. The first is an optimal one based on the exact calculation of likelihood functions, and the second is a quasi-optimal algorithm based on using the Gaussian approximation of likelihood functions. These algorithms were simulated for different variants of the specified values of deforming distortions in the form of determinate functions and in the form of random function realizations. The experimental error probability was compared with its theoretical estimate at different values of signal-to-noise ratio.
Original Russian Text © A.V. Akimov and A.A. Sirota, 2017, published in Izvestiya Vysshikh Uchebnykh Zavedenii, Radioelektronika, 2017, Vol. 60, No. 10, pp. 592–604.This study was carried out within the framework of the State Assignment of the Ministry of Education and Science of the Russian Federation in project No. 8.3844.2017/4.6 “Development of Tools of Express Analysis and Classification of Elements of Nonuniform Flow of Grain Mixtures with Pathologies Based on the Integration of Spectral Analysis and Machine Learning Methods”.
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