Iterative Method of Radiosignals Detection based on Decision Statistics

Authors

DOI:

https://doi.org/10.20535/RADAP.2020.81.11-20

Keywords:

operator, decisive statistics, signal-to-noise ratio, narrowband signal, iterative method, threshold

Abstract

Intensive computerization of radio systems stimulates the use of various technologies in radar and communication systems, which makes it difficult to solve the problem of signal detection and requires the use of modern processing algorithms in radio monitoring systems with the possibility of modifying their parts according to a specific type of signal and interference. To solve this problem, an iterative method for detecting radio signals based on decisive statistics was developed. The key idea of the proposed method is using of statistical differences between the signal and noise not according to the values of the samples of the received signal, but according to some decisive statistics from them. The essence of the method is to transform the sample of the received radio signal using some operator, followed by the calculation of the decisive statistics and its comparison with some threshold. When the threshold is exceeded, the maximum value of the sample from the converted sample is discarded and the procedure is repeated until the value of the decision statistics becomes less than the threshold. The discarded samples refer to the signal, and the rest to noise. The type of operator is selected based on a priori information about the signal and increases its contrast against random noise. Decisive statistics should have small scattering characteristics, and the distance between its values for noise and the signal mixture should be as large as possible for a given signal-to-noise ratio. A study of the developed method for detecting narrowband signals in the frequency domain showed that the Fourier transform is the optimal form of the operator, and the coefficient of variation is optimal decisive statistic. The developed iterative method for the frequency domain allows detecting narrowband signals at unknown values of noise power in the dynamic range, which is limited only by the level of the side lobes of the window function, when the analysis frequency band is loaded no less than 60%.

Author Biography

M. V. Buhaiov , Zhytomyr military institute named after S. P. Korolyov

Cand. Sci (Tech)

References

Перелік посилань

Qiu Е, Guo. Y. Signal Processing and Data Analysis. Walter de Gruyter GmbH, Berlin/Boston, 2018. 580 p.

Napolitano А. Generalizations of cyclostationary signal processing. Spectral analysis and applications. John Wiley & Sons Ltd., 2012. 492 р.

Pace P. E. Detecting and Classifying Low Probability of Intercept Radar. Second Edition. Artech house, 2009. 893 р.

Boashash В. Time-frequency signal analysis and process-ing. A comprehensive reference. Elsevier Ltd, 2003. 743 р.

Дятлов А. П., Кульбикаян Б. Х. Корреляционная обработка широкополосных сигналов в автоматизированных комплексах радиомониторинга. Москва: Горячая линия–Телеком, 2010. 332 с.

Carillo R. E., Polania L. F., Barnen K. E. Iterative algorithms for compressed sensing with partially known support // ICASSP, 2010. P. 3654–3657.

Wang Y., Yin W. Sparse Signal Reconstruction via Iterative Support Detection // SIAM Journal on Imaging Sciences, 2010. N. 3. P. 462–491. doi. 10.1137/090772447

Pun M., Morelli M., Kuo C. J. Iterative Detection and Frequency Synchronization for OFDMA Uplink Transmissions // IEEE Transactions on wireless communications, 2007. Vol. 6, N. 2. P. 629–639.

Feng H., Zhao X., Li Z., Xing S. A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems // Electronics, 2018. Vol. 8. P. 1–13. doi:10.3390/electronics80

Shaghaghi M., Vorobyov S. A. Iterative root-MUSIC algorithm for DOA estimation // 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2013. P. 53–56.

Choi J. Adaptive and Iterative Signal Processing in Communication. Cambridge University Press, 2006. 336 p.

Lin F., Qui R. C., Browning J. P. Spectrum Sensing With Small-Sized Data Sets in Cognitive Radio: Algorithms and Analysis // IEEE transactions on vehicular technology, 2015. Vol. 64, N. 1. P. 77−87.

Hu X-L.,Ho P-H., Peng L. Statistical Properties of Energy Detection for Spectrum Sensing by Using Estimated Noise Variance // Journal of Sensors and Actuator Networks, 2019. N. 8 (28). P. 1–22. doi:10.3390/jsan8020028

Bozovic R., Simic M. Spectrum Sensing Based on Higher Order Cumulants and Kurtosis Statistics Tests in Cognitive Radio // Radioengineering, 2019. Vol. 28, N 2. P. 464–472. doi: 10.13164/re.2019.0464

Negi B. S., Singh O., Khairnan C. Enhancing Entropy Based Spectrum Sensing using Eigen Value Decomposition in Cognitive Radio Networks // International Journal of Engineering Research and Technology, 2019. Vol. 12, N. 7. P. 1008–1013.

Zhang Y. L., Zhang Q. L., Melodia T. A Frequency-Domain Entropy-Based Detector for Robust Spectrum Sensing in Cognitive Radio Networks // IEEE communications letters, 2010. Vol. 14, N. 6. P. 533–535.

Molina-Tenorio Y., Prieto-Guerrero A., Aguilar-Gonzalez R. A Novel Multiband Spectrum Sensing Method Based on Wavelets and the Higuchi Fractal Dimension // Radioengineering, 2019. Vol. 19, N 1322. P. 1–20.

Бугайов М. В. Узагальнений енергетичний детектор з ітеративним обробленням вузькосмугових сигналів у частотній області // Вісник НТУУ "КПІ". Серія Радіотехніка, Радіоапаратобудування. Київ: КПІ, 2019. № 78. С. 27–35. doi: https://doi.org/10.20535/RADAP.2019.78.27-35

Технический анализ сигналов и распознавание радиоизлучений. С.–Пб.: ВАС, 1998. 368 с.

Poularikas A. D. Transforms and applications. Handbook. CRC Press Taylor & Francis Group, 2010. 914 p.

Egner S., Püschel M. Automatic Generation of Fast Discrete Signal Transforms // IEEE Transactions on Signal Processing, 2001. Vol. 49, N. 9. P. 1992–2002.

Бугайов М. В. Рекурсивний алгоритм обчислення коефіцієнта варіації // Теоретичні та прикладні аспекти радіотехніки, приладобудування і комп’ютерних технологій. Матеріали IV міжнар. наук.-техн. конф., 20−21 червня 2019 року: збірник тез доповідей. Тернопіль : ФОП Паляниця В. А. , 2019. С. 85−86.

item Способ автоматического обнаружения узкополосных сигналов: пат. 2479920 Российская Федерация, МПК Н04В 1/10/ Т. Е. Алексеева; заявитель и патентообладатель Военная академия связи имени Маршала Советского Союза С. М. Буденного. № 2011128870/07; заявл. 12.07.2011; опубл. 20.04.2013, Бюл. № 11. 10 с.

References

Qiu Е, Guo. Y. Signal Processing and Data Analysis. Walter de Gruyter GmbH, Berlin/Boston, 2018. 580 p.

Napolitano A. (2012) Generalizations of Cyclostationary Signal Processing: Spectral analysis and applications, John Wiley & Sons Ltd., 2012. 492 р. DOI: 10.1002/9781118437926

Pace P. E. Detecting and Classifying Low Probability of Intercept Radar. Second Edition. Artech house, 2009. 893 р.

Boashash В. Time-frequency signal analysis and processing. A comprehensive reference. Elsevier Ltd, 2003. 743 р.

Dyatlov A. P., Kulbikayan B. H. Correlation processing of broadband signals in automated radio monitoring systems. [Korrelyatsionnaya obrabotka shirokopolosnyih signalov v avtomatizirovannyih kompleksah radiomonitoringa]/ Moskow: Goryachaya liniya–Telekom, 2010. 332 p.

Carrillo R.E., Polania L.F. and Barner K.E. (2010) Iterative algorithms for compressed sensing with partially known support. 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3654–3657. DOI: 10.1109/icassp.2010.5495901

Wang Y. and Yin W. (2010) Sparse Signal Reconstruction via Iterative Support Detection. SIAM Journal on Imaging Sciences, Vol. 3, Iss. 3, pp. 462-491. DOI: 10.1137/090772447

Pun M., Morelli M. and Kuo C. (2007) Iterative detection and frequency synchronization for OFDMA uplink transmissions. IEEE Transactions on Wireless Communications, Vol. 6, Iss. 2, pp. 629-639. DOI: 10.1109/twc.2007.05368

Feng H., Zhao X., Li Z. and Xing S. (2019) A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems. Electronics, Vol. 8, Iss. 9, pp. 980. DOI: 10.3390/electronics8090980

Shaghaghi M. and Vorobyov S.A. (2013) Iterative root-MUSIC algorithm for DOA estimation. 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 53–56. DOI: 10.1109/camsap.2013.6714005

Choi J. (2006) Adaptive and Iterative Signal Processing in Communications, Cambridge University Press, 336 p. DOI: 10.1017/cbo9780511607462

Lin F., Qiu R.C. and Browning J.P. (2015) Spectrum Sensing With Small-Sized Data Sets in Cognitive Radio: Algorithms and Analysis. IEEE Transactions on Vehicular Technology, Vol. 64, Iss. 1, pp. 77-87. DOI: 10.1109/tvt.2014.2321388

Hu X., Ho P. and Peng L. (2019) Statistical Properties of Energy Detection for Spectrum Sensing by Using Estimated Noise Variance. Journal of Sensor and Actuator Networks, Vol. 8, Iss. 2, pp. 28. DOI: 10.3390/jsan8020028

Bozovic R. and Simic M. (2019) Spectrum Sensing Based on Higher Order Cumulants and Kurtosis Statistics Tests in Cognitive Radio. Radioengineering, Vol. 28, Iss. 2, pp. 464-472. DOI: 10.13164/re.2019.0464

Negi B. S., Singh O., Khairnan C. Enhancing Entropy Based Spectrum Sensing using Eigen Value Decomposition in Cognitive Radio Networks // International Journal of Engineering Research and Technology, 2019. Vol. 12, N. 7. P. 1008–1013.

Zhang Y., Zhang Q. and Melodia T. (2010) A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks. IEEE Communications Letters, Vol. 14, Iss. 6, pp. 533-535. DOI: 10.1109/lcomm.2010.06.091954

Molina-Tenorio Y., Prieto-Guerrero A. and Aguilar-Gonzalez R. (2019) A Novel Multiband Spectrum Sensing Method Based on Wavelets and the Higuchi Fractal Dimension. Sensors, Vol. 19, Iss. 6, pp. 1322. DOI: 10.3390/s19061322

Buhaiov M.V. (2019) Generalized Energy Detector with Iterative Processing of Narrowband Signals in Frequency Domain. Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, Iss. 78, pp. 27-35. DOI: 10.20535/radap.2019.78.27-35

Technical analysis of signals and recognition of radio emissions [Tehnicheskiy analiz signalov i raspoznavanie radioizlucheniy]. S. –Pb. : Military Academy of Communication, 1998. 368 p.

Poularikas A. D. Transforms and applications. Handbook. CRC Press Taylor & Francis Group, 2010. 914 p.

Egner S. and Puschel M. (2001) Automatic generation of fast discrete signal transforms. IEEE Transactions on Signal Processing, Vol. 49, Iss. 9, pp. 1992-2002. DOI: 10.1109/78.942628

Buhaiov M. V. Recursive algorithm for calculating the coefficient of variation [Rekursyvnyi alhorytm obchyslennia koefitsiienta variatsii] // ''Theoretical and applied aspects of radio engineering, instrumentation and computer technology''. Materials IV int. scientific-technical conf., June 20-21, 2019: Abstracts. Ternopil: 2019. P. 85−86.

A method for automatically detecting narrowband signals: Pat. 2479920 Russian Federation, IPC Н04В 1/10 / T. E. Alekseeva; Applicant and patent holder Military Academy of Communications named after Marshal of the Soviet Union S. M. Budyonny. No. 2011128870/07; declared 07/12/2011; publ. 04/20/2013, Bull. No. 11. 10 p.

Published

2020-06-30

How to Cite

Бугайов, М. В. (2020) “Iterative Method of Radiosignals Detection based on Decision Statistics”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (81), pp. 11-20. doi: 10.20535/RADAP.2020.81.11-20.

Issue

Section

Telecommunication, navigation, radar systems, radiooptics and electroacoustics

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