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Journal Radioengineering №2 for 2024 г.
Article in number:
Simulation model of compression of a signal on the basis of the Compressive Sensing method
Type of article: scientific article
DOI: https://doi.org/10.18127/j00338486-202402-03
UDC: 004.93'1; 51-74; 519.254
Authors:

L.I. Dvoiris, I.N. Kryukov, A.N. Tolmachev

Abstract:

Detection and recognition of signals in time and frequency domain at different protection of objects and boundaries, carried out on the basis of the geographically distributed systems of protection, is the important task solved with use of sensors on the basis of different the physical principles. Detection and recognition of signals the specified means comes down to processing of big arrays of digital data. Their high-speed performance, energy consumption, mass-dimensional and other characteristics depend on algorithms of processing of these data. In digital signal processing (Digital signal processing DSP) achieved great success in algorithm elaboration for collecting and information extraction from signals. Digitization allowed to create more reliable detection systems and data processings, flexible and cheaper, than analog. At the same time at digital signal processing there is a need of work with more and more high frequencies of sampling, processing, the analysis and storage of large volumes of resultants of data. If the problem of work with high frequencies of sampling could be partially solved at the hardware level using expensive architecture of ATsP consuming high power until recently, then processing and data analysis are even more often carried out on the basis of researches in the field of compression and reduction of dimension of the processed data on the basis of the Compressive Sensing method.

Pages: 19-24
For citation

Dvoiris L.I., Kryukov I.N., Tolmachev A.N. Simulation model of compression of a signal on the basis of the Compressive Sensing method. Radiotekhnika. 2024. V. 88. № 2. P. 19−24. DOI: https://doi.org/10.18127/j00338486-202402-03 (In Russian)

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Date of receipt: 26.12.2023
Approved after review: 10.01.2024
Accepted for publication: 29.01.2024