COMPARATIVE ANALYSIS OF ALGORITHMS FOR DETECTING OF UNKNOWN SIGNALS WITH A GIVEN SET OF QUALITY INDICATORS

The subject of this article is the problem of detecting unknown signals in conditions of high a priori uncertainty, which can occur during the determination of unoccupied frequency channels in cognitive networks. It should be noted that various sources of radio emissions work on the air, which in tu...

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Main Authors: Валерий Михайлович Безрук, Станислав Андреевич Иваненко
Format: Article
Language:English
Published: National Aerospace University «Kharkiv Aviation Institute» 2018-10-01
Series:Радіоелектронні і комп'ютерні системи
Subjects:
Online Access:http://nti.khai.edu/ojs/index.php/reks/article/view/52
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author Валерий Михайлович Безрук
Станислав Андреевич Иваненко
author_facet Валерий Михайлович Безрук
Станислав Андреевич Иваненко
author_sort Валерий Михайлович Безрук
collection DOAJ
description The subject of this article is the problem of detecting unknown signals in conditions of high a priori uncertainty, which can occur during the determination of unoccupied frequency channels in cognitive networks. It should be noted that various sources of radio emissions work on the air, which in turn complicates the solution of the problem of detection, since it is impossible to say what kind of signal will be received. Most existing algorithms require information about the signals for their operation. In practice, it is not possible to have such data on all sources of radio emission due to their diversity. The goal of the article is to study non-classical signal detection algorithms in conditions of high a priori uncertainty, when there is information only about noise, and signals are unknown.  The task: to conduct a comparative analysis of unknown signal detection algorithms based on a set of quality indicators and to determine the set of Pareto-optimal detection algorithms, as well as to identify the best algorithm for a set of quality indicators.  The method of statistical modeling of detection algorithms on samples of real signals and noise is performed. As a result, we obtained estimates of speed of work and quality of signal detection for a number of different variants of unknown signal detection algorithms. Possible variants of implementation of the detectors were summarized in the table. These variants were formed taking into account the dimension of the DPF sample and the number of implementations on which the decision is made. A comparative analysis of different types of detection algorithms is carried out taking into account the set of performance indicators and the quality of signal detection. It should be noted that the values of quality indicators of detection of unknown signals and performance indicators of the algorithms are related and contradictory. Conclusions. A multicriteria selection of a subset of Pareto-optimal variants is performed. Using the conditional preference criterion, the only preferred variant of the algorithm for detecting unknown signals is selected from the Pareto subset. The results of the research can be used in automated radio monitoring in cognitive radio networks
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spelling doaj.art-8db543b815f344ac9f6f0da4871854472023-08-02T04:27:09ZengNational Aerospace University «Kharkiv Aviation Institute»Радіоелектронні і комп'ютерні системи1814-42252663-20122018-10-0102677410.32620/reks.2018.2.0751COMPARATIVE ANALYSIS OF ALGORITHMS FOR DETECTING OF UNKNOWN SIGNALS WITH A GIVEN SET OF QUALITY INDICATORSВалерий Михайлович Безрук0Станислав Андреевич Иваненко1Харьковский национальный университет радиоэлектроникиХарьковский национальный университет радиоэлектроникиThe subject of this article is the problem of detecting unknown signals in conditions of high a priori uncertainty, which can occur during the determination of unoccupied frequency channels in cognitive networks. It should be noted that various sources of radio emissions work on the air, which in turn complicates the solution of the problem of detection, since it is impossible to say what kind of signal will be received. Most existing algorithms require information about the signals for their operation. In practice, it is not possible to have such data on all sources of radio emission due to their diversity. The goal of the article is to study non-classical signal detection algorithms in conditions of high a priori uncertainty, when there is information only about noise, and signals are unknown.  The task: to conduct a comparative analysis of unknown signal detection algorithms based on a set of quality indicators and to determine the set of Pareto-optimal detection algorithms, as well as to identify the best algorithm for a set of quality indicators.  The method of statistical modeling of detection algorithms on samples of real signals and noise is performed. As a result, we obtained estimates of speed of work and quality of signal detection for a number of different variants of unknown signal detection algorithms. Possible variants of implementation of the detectors were summarized in the table. These variants were formed taking into account the dimension of the DPF sample and the number of implementations on which the decision is made. A comparative analysis of different types of detection algorithms is carried out taking into account the set of performance indicators and the quality of signal detection. It should be noted that the values of quality indicators of detection of unknown signals and performance indicators of the algorithms are related and contradictory. Conclusions. A multicriteria selection of a subset of Pareto-optimal variants is performed. Using the conditional preference criterion, the only preferred variant of the algorithm for detecting unknown signals is selected from the Pareto subset. The results of the research can be used in automated radio monitoring in cognitive radio networkshttp://nti.khai.edu/ojs/index.php/reks/article/view/52радиомониторингобнаружение неизвестных сигналовстатистическое моделированиепоказатели быстродействия и качества обнаружениямногокритериальная оптимизация
spellingShingle Валерий Михайлович Безрук
Станислав Андреевич Иваненко
COMPARATIVE ANALYSIS OF ALGORITHMS FOR DETECTING OF UNKNOWN SIGNALS WITH A GIVEN SET OF QUALITY INDICATORS
Радіоелектронні і комп'ютерні системи
радиомониторинг
обнаружение неизвестных сигналов
статистическое моделирование
показатели быстродействия и качества обнаружения
многокритериальная оптимизация
title COMPARATIVE ANALYSIS OF ALGORITHMS FOR DETECTING OF UNKNOWN SIGNALS WITH A GIVEN SET OF QUALITY INDICATORS
title_full COMPARATIVE ANALYSIS OF ALGORITHMS FOR DETECTING OF UNKNOWN SIGNALS WITH A GIVEN SET OF QUALITY INDICATORS
title_fullStr COMPARATIVE ANALYSIS OF ALGORITHMS FOR DETECTING OF UNKNOWN SIGNALS WITH A GIVEN SET OF QUALITY INDICATORS
title_full_unstemmed COMPARATIVE ANALYSIS OF ALGORITHMS FOR DETECTING OF UNKNOWN SIGNALS WITH A GIVEN SET OF QUALITY INDICATORS
title_short COMPARATIVE ANALYSIS OF ALGORITHMS FOR DETECTING OF UNKNOWN SIGNALS WITH A GIVEN SET OF QUALITY INDICATORS
title_sort comparative analysis of algorithms for detecting of unknown signals with a given set of quality indicators
topic радиомониторинг
обнаружение неизвестных сигналов
статистическое моделирование
показатели быстродействия и качества обнаружения
многокритериальная оптимизация
url http://nti.khai.edu/ojs/index.php/reks/article/view/52
work_keys_str_mv AT valerijmihajlovičbezruk comparativeanalysisofalgorithmsfordetectingofunknownsignalswithagivensetofqualityindicators
AT stanislavandreevičivanenko comparativeanalysisofalgorithmsfordetectingofunknownsignalswithagivensetofqualityindicators