Automatic modulation classification technique for radio monitoring

Weber, C.; Mitrouchev, Peter; Felhauer, T.

doi:10.1049/el.2015.0610

Cited by 29 publications

(20 citation statements)

References 4 publications

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“…And its training samples for several reasons cannot be obtained. This complicates the solution of signal recognition problems in ARM and determines the relevance of using special methods of signal recognition in conditions of high a priori uncertainty, which differ from the known methods [5].…”

Section: The Decisive Rule Of Selection and Recognition Of Specified mentioning

confidence: 99%

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Selection and Recognition of the Specified Radio Signals in the Sw Band

Bezruk¹,

Ivanenko

2018

Information and Telecommunication Sciences

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Background. Currently, the wireless radio technologies are an intensively developing branch of the telecommunications industry, due to their high popularity as a result of their convenience for civilian and specialized applications. Currently, the frequency resources are already quite overloaded, which leads to electromagnetic compatibility problems. This problem can be solved by efficient use of the frequency resource; for this purpose, monitoring of the radio frequency resource use by means of automated radio monitoring is carried out. Now a lot of radio-electronic means are operating at the moment and their number is rapidly increasing every day, which can lead to the fact that not only radio signals can be received, but also unknown signals, which in turn can lead to recognition errors. The paper studies the algorithms of selection and recognition of given radio signals, which take into account the class of unknown signals. Objective. The aim of the paper is to research the quality indicators of the selection and recognition algorithms for given radio signals in the presence of unknown signals during radio monitoring in the SW band. Methods. Investigation of algorithms for selection and recognition of specified radio signals in the presence of unknown signals by the simulation method on real signal samples. Results. The values of quality indicators of selection and recognition of specified radio signals in the presence of unknown signals that are acceptable for the practice of radio monitoring are obtained. The proposed recognition algorithms should improve the quality of radio monitoring results, and thereby improve the results of radio monitoring. Conclusions. The proposed algorithms for the selection and recognition of specified radio signals in the presence of unknown signals will improve the quality of radio monitoring in the SW band.

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Section: The Decisive Rule Of Selection and Recognition Of Specified mentioning

confidence: 99%

“…It is also possible that the detected signal doesn't belong to any of the known signal classes, which is a priori unknown, and information about it is missing in the database. It should be noted that in this problem statement of signal recognition with an automated RM wasn't solved [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Selection and Recognition of the Specified Radio Signals in the Sw Band

Bezruk¹,

Ivanenko

2018

Information and Telecommunication Sciences

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“…Одной из важных задач контроля за использованием радиочастотного спектра является проведение автоматизированного радиомониторинга (РМ) [1][2][3][4][5]. Следует отметить, что при автоматизированном РМ из-за действия шума и многих других неконтролируемых факторов сигналы в частотных каналах носят случайный характер с априори неизвестными статистическими характеристиками.…”

Section: Introductionunclassified

Обнаружение И Распознавание Сигналов В Условиях Повышенной Априорной Неопределенности В Задачах Радиомониторинга

Безрук¹,

Ivanenko²

2018

CNCS

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В данной работе рассматриваются неклассические методы обнаружения и распознавания сигналов в условиях повышенной априорной неопределенности, которые основаны на разных вероятностных моделях сигналов и помех. Проведены исследования методов обработки сигналов путем статистических испытаний на выборках реальных сигналов и шумов, характерных для РМ. В результате исследований получены оценки вероятностей правильного обнаружения и распознавания сигналов, приемлемые для работы в комплексах автоматизированного РМ.

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“…The approach based on clustering algorithms is a new trend in Automatic Modulation Classification (AMC) for digital modulations. An advanced method derived from -means algorithm is proposed by Weber et al [17] for Quadrature Amplitude Modulation (QAM) and PSK signals. In this paper, a novel utility function which indicates the best fitting constellation diagram is defined for the AMC decision.…”

Section: Introductionmentioning

confidence: 99%

“…. (17) Assume that ( ) is distributed independently and has an equal occurrence probability. Since ( ) is a stationary Gaussian phase noise, formula (17) can transform into…”

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confidence: 99%

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

et al. 2016

Mobile Information Systems

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Promptitude and accuracy of signals’ non-data-aided (NDA) identification is one of the key technology demands in noncooperative wireless communication network, especially in information monitoring and other electronic warfare. Based on this background, this paper proposes a new signal classifier for phase shift keying (PSK) signals. The periodicity of signal’s phase is utilized as the assorted character, with which a fractional function is constituted for phase clustering. Classification and the modulation order of intercepted signals can be achieved through its Fast Fourier Transform (FFT) of the phase clustering function. Frequency offset is also considered for practical conditions. The accuracy of frequency offset estimation has a direct impact on its correction. Thus, a feasible solution is supplied. In this paper, an advanced estimator is proposed for estimating the frequency offset and balancing estimation accuracy and range under low signal-to-noise ratio (SNR) conditions. The influence on estimation range brought by the maximum correlation interval is removed through the differential operation of the autocorrelation of the normalized baseband signal raised to the power ofQ. Then, a weighted summation is adopted for an effective frequency estimation. Details of equations and relevant simulations are subsequently presented. The estimator proposed can reach an estimation accuracy of10-4even when the SNR is as low as-15 dB. Analytical formulas are expressed, and the corresponding simulations illustrate that the classifier proposed is more efficient than its counterparts even at low SNRs.

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Automatic modulation classification technique for radio monitoring

Cited by 29 publications

References 4 publications

Selection and Recognition of the Specified Radio Signals in the Sw Band

Selection and Recognition of the Specified Radio Signals in the Sw Band

Обнаружение И Распознавание Сигналов В Условиях Повышенной Априорной Неопределенности В Задачах Радиомониторинга

Phase Clustering Based Modulation Classification Algorithm for PSK Signal over Wireless Environment

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