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Techniques of sampling the energy characteristics of two-dimensional random signals
V.V. Syuzev 1, A.V. Proletarsky 1, D.A. Mikov 1, I.I. Deykin 1

Bauman Moscow State Technical University, 105005, Moscow, Russia, 2nd Baumanskaya street, 5/1

 PDF, 1340 kB

DOI: 10.18287/2412-6179-CO-1074

Pages: 828-839.

Full text of article: Russian language.

The article is devoted to methods of discretization of energy characteristics of two-dimensional random signals when simulating random signals using the original harmonic method, which is a generalization of the well-known algorithm proposed by V. S. Pugachev for the two-dimensional case. Requirements imposed on the sampling method are aimed at reducing the computational complexity of the simulation method and increasing its flexibility thanks to removing restrictions on the form of autocorrelation functions and spectral energy density functions. The use of the simulation error as a criterion for quality assessment is proposed. The discretization method is considered for signals given both on unlimited definition intervals and on limited ones. The article demonstrates results of the software system implementation in which the original simulation method is realized using the described sampling methods in both cases. The proposed technique is shown to be robust and efficient, with the results obtained being of independent scientific and technical value and showing promise for developing new effective spectral techniques of simulating signals for the use in intelligent decision support systems.

random two-dimensional signal, modeling and simulation of signals, Pugachev's algorithm, harmonic Fourier bases, energy characteristics of signals, energy spectral density function, autocorrelation function, intelligent decision support systems, ultra-fast information processing.

Syuzev VV, Proletarsky AV, Mikov DA, Deykin II. Techniques of sampling the energy characteristics of two-dimensional random signals. Computer Optics 2022; 46(5): 828-839. DOI: 10.18287/2412-6179-CO-1074.

This work was financially supported by the Russian Federation Ministry of Science and Higher Education under the government project on "Fundamental research of methods of digital transformation of components for micro- and nano-systems" (Project # 0705-2020- 0041).


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