SELECTION AND RECOGNITION OF THE SPECIFIED RADIO EMISSIONS BASED ON THE AUTOREGRESSION SIGNAL MODEL

Authors

  • V. М. Bezruk Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
  • N. М. Kaliuznyi National University of Radio Electronics, Kharkiv, Ukraine
  • Guo Qiang Harbin Engineering University, Harbin, P.R., China
  • Yu Zheng Qingdao University, Chair of Department Micro- and Nano- Electronics, Qingdao, P.R., China
  • I. М. Nikolaev Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2020-2-1

Keywords:

Automated radio monitoring, radio emission, autoregressive model, selection, recognition, decisive rule, statistical tests, recognition system.

Abstract

Context. A solution to the relevance problem of selecting and recognizing specified radio emissions in the presence of unknown radio emissions in automated radio monitoring is considered. It is proposed to solve the problem in an unconventional method for the recognition of statistically specified random signals in the presence of a class of unknown signals.

Objective. The goal of the work is іnvestigation of the possibility of using random signal recognition methods in conditions of increased a priori uncertainty to solve the problem. The features of the signal recognition method are discussed, as well as the results of a study of the recognition quality indicators of given radio emissions, which are obtained by statistical modeling on samples of the corresponding signals.

Method. The recognition method is based on the description of signals by a probabilistic model in the form of Gaussian autoregressive processes. It is proposed to use the new decision rule for the selection and recognition of statistically specified signals in the presence of unknown signals class. The proposed method of signal selection and recognition can be implemented in a recognition system that operates in training and recognition modes. In the training mode, unknown parameters of the decision rule are evaluated by classified samples of the given signals.

Results. Research conducted by statistical tests on samples of the corresponding signals characteristic of automated radio monitoring of radio communications equipment. Practical results of studies of the problem of selection and recognition of specified radio emissions are presented. Values of indicators of quality of radio emissions recognition acceptable for the practice of radio monitoring are obtained. The dependences of quality indicators on some conditions and recognition parameters are investigated.

Conclusions. Undertaken studies showed possibility of decision of problem by application of an unconventional method  of selection and recognition of specified random signals. The practical significance lies in obtaining recommendations on the construction of systems for the recognition of radio emissions for specialists in the design of automated radio monitoring complexes. Such signal recognition systems are implemented by computer technology and is adaptive. The structure and parameters of the systems are set according to the samples of signals that are obtained for the corresponding given radio emissions. 

Author Biographies

V. М. Bezruk, Kharkiv National University of Radio Electronics, Kharkiv

Dr. Sc., Professor, Head of the Department Information and Network Engineering

N. М. Kaliuznyi, National University of Radio Electronics, Kharkiv

PhD, Senior Scientist, Head of the problem research laboratory, senior Lecturer of the
Department of Information and Network Engineering, Kharkiv

Guo Qiang, Harbin Engineering University, Harbin, P.R.

PhD, Professor, College of Information and Telecommunication

Yu Zheng, Qingdao University, Chair of Department Micro- and Nano- Electronics, Qingdao, P.R.

PhD, Professor

I. М. Nikolaev, Kharkiv National University of Radio Electronics, Kharkiv

PhD, Senior Research, Leading Researcher of the problem research laboratory

References

Ashihmin A. V., Kozmin V. A., Rembovskiy A. M. Radiomonitoring: zadachi, metody, sredstva [Radio monitoring: tasks, methods, tools]. Moscow, Goryachaya liniya. Telekom, 2015, 640 р. (Russian)

Weber C., Peter M., Felhauer T. Automatic modulation classification technique for radio monitoring, Electronics Letters, 2015, Vol. 51, No. 10, pp. 794–796. DOI: 10.1049/el.2015.0610

Kipenskiy A., Zadonskiy O., Kaliuzhniy M., Qiang Guo Creating the Information Basis of Spectral Masks for Automated Radiomonitoring, Problems of Infocommunications, Science and Technology (PIC S&T), IEEE International Scientific-Practical Conference, 8–11 October 2019. Kiev, Ukraine, proceedings IEEE Xplore, 2020, pp. 167–169. DOI: 10.1109/PICST47496.2019.9061359

Yingkun Huang, Weidong Jin, Bing Li, Peng Ge, Yunpu Wu Automatic modulation recognition of radar signals based on manhattan distance-based features, Access IEEE, 2019, Vol. 7, pp. 41193–41204. DOI: 10.1109/ACCESS.2019.2907159

Zhilu Wu, Siyang Zhou, Zhendong Yin, Bo Ma, Zhutian Yang Robust Automatic Modulation Classification Under Varying Noise Conditions, Access IEEE, 2017, Vol. 5, pp. 19733–19741. DOI: 10.1109/ACCESS.2017.2746140

Tekbiyik Kürşat, Akbunar Özkan, Riza Ekti Ali, Görçin Ali, Karabulut Kurt Güneş Multi-Dimensional Wireless Signal Identification Based on Support Vector Machines, Access IEEE, 2019, Vol. 7, pp. 138890–138903. DOI 10.1109/ACCESS.2019.2942368

Cheng Yuanzeng, Zhang Hailong, Wang Y. Research on Modulation Recognition of the Communication Signal Based on Statistical Model, Third International Conference on Measuring Technology and Mechatronics Automation, 2011, Vol. 3, pp. 46–50. DOI 10.1109/ICMTMA.2011.583

Hassan K., Dayoub I., Hamouda W., Berbineau M. Automatic modulation recognition using wavelet transform and neural network, Intell. Transp. Syst. Telecommun, 9th International Conference: proceedings, 2009, pp. 234–238. DOI: 10.1109/ITST.2009.5399351

Yili Li, Kon Max Wong Riemannian Distances for Signal Classification by Power Spectral Density, Selected Topics in Signal Processing of IEEE Journal, 2013, Vol. 7, No. 4, pp. 655–669. DOI: 10.1109/JSTSP.2013.2260320.

Watanabe S. Methodologies of pattern recognition, Academic Press, 1969, 590 р. DOI: https://doi.org/10.1016/C2013-0-12340-9

Hau C. C. Handbook of pattern recognition and computer vision, World Scientific, 2015, P. 584.

Borglund A. Statistical Pattern Recognition, International Journal of Computer, 2014, Vol. 7, No. 1, pp. 120–128.

Merkov A. B. Raspoznavanie obrazov. Vvedenie v metodyi statisticheskogo obucheniya [Pattern recognition. Introduction to Statistical Learning Methods]. Moscow, Editorial URSS, 2011, 256 p. (Russian)

Marchenko B. G., Omelchenko V. A. Veroyatnostnyye modeli sluchaynykh signalov i poley v prikladnoy statisticheskoy radiofizike [Probabilistic models of random signals and fields in applied statistical radiophysics]. Kiev, UMK VO, 1988, 176 p. (Russian)

Omelchenko V. A. Foundations the spectral theory of signal recognition. Kharkov, Vysha Shkola, 1983, Р. 156. (Russian)

Bezruk V., Fedorov O., Іvanenko S., Nemec Z., Pidanic J. Detection and recognition of signals in HF radio monitoring, 19-th Conference on microwave techniques (COMITE-2019, Czech Republlic): proceedings, 2019, pp. 293–297. DOI: 10.1109/RADIOELEK.2019.8733445

Bezruk V. M., Pevtsov G. V. Teoreticheskie osnovy proektirovaniya sistem raspoznavaniya signalov dlya avtomatizirovannogo radiokontrolya [Theoretical Foundations of Designing Signal Recognition Systems for Automated Radio Monitoring]. Harkov, Kollegium, 2006, 430 p.

Bezruk V. M. Recognition Methods Based on Autoregression Model of Signals, Telecommunications and Radio Engineering, 2002, Vol. 58(3&4), pp. 12–18. DOI: 10.1615/TelecomRadEng.v58.i3-4.20

Bezruk V., Omelchenko A., Fedorov O., Mercorelli P., Hipólito J. N. Selection and Recognition of Statistically Defined Signals in Learning Systems, 44-th Annual Conference of the IEEE Industrial Electronics Society (IECON-2018, USA), proceedings, 2018, pp. 3211–3216. DOI: 10.1109/IECON.2018.859132

Downloads

How to Cite

Bezruk V. М., Kaliuznyi N. М., Qiang, G., Zheng, Y., & Nikolaev I. М. (2020). SELECTION AND RECOGNITION OF THE SPECIFIED RADIO EMISSIONS BASED ON THE AUTOREGRESSION SIGNAL MODEL. Radio Electronics, Computer Science, Control, (2), 7–14. https://doi.org/10.15588/1607-3274-2020-2-1

Issue

Section

Radio electronics and telecommunications