RECOGNITION METHOD OF SPECIFIED TYPES OF SIGNAL MODULATION BASED ON A PROBABILISTIC MODEL IN THE FORM OF A MIXTURE OF DISTRIBUTIONS

Authors

  • V.V. Bezruk Kharkiv National University of Radio Electronics, Ukraine
  • M. M. Kaliuzhnyi Kharkiv National University of Radio Electronics, Kharkiv, Ukraine., Ukraine
  • V.V. Semenets Kharkiv National University of Radio Electronics, Kharkiv, Ukraine., Ukraine
  • Qiang Guo Harbin Engineering University, China
  • Yu Zheng Qingdao University, The People Republic of China., China

DOI:

https://doi.org/10.15588/1607-3274-2021-4-1

Keywords:

automated radio monitoring, radio emission, signal, types modulation, probabilistic model, recognition method, statistical tests. the probability of correct recognition.

Abstract

Context. The article considers the features of solving non-traditional problems of recognition of specified types modulation signals in automated radio monitoring. The practical features of this problem determine the increased a priori uncertainty, which consists in the absence of a priori information about the distribution densities of the given signals and the presence of unknown signals.

Objective. It is proposed to solve the problem using an unconventional method for the recognition of statistically specified random signals in the presence of a class of unknown signals. This method assumes that for the given signals there is a classified training sample of realizations, according to which the unknown parameters of their distributions are estimated, as well as some threshold values that determine the probabilities of correct recognition of the given types of signal modulation in the presence of unknown signals.

Method. A general solution to the problem of recognition of given signals in the presence of unknown signals is given, and recognition methods of types modulation based on the description of signals by probabilistic model in the form of a mixture of distributions are given. The method is based on the description of signals by a probabilistic model in the form of a mixture of distributions and construction of a closed area for given signals in the probabilistic space of signals.

Results. Studies of the recognition problems of given types of modulation of signals have been carried out. The studies were performed by statistical tests on samples of signals for radio monitoring of communications. In this case, the decisive rule for recognizing the given types of signal modulation is implemented in software on a computer. As a result of the statistical tests carried out on control samples of signals, estimates of the probabilities of correct recognition of the given types of signal modulation in the presence of unknown signals were obtained.

Conclusions. 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 property. As a result of the research, practical recommendations were obtained on the use of the proposed method for recognizing specified types of signal modulation in automated radio monitoring systems.

Author Biographies

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

Dr Sc., Professor, Head of the Department

M. M. Kaliuzhnyi, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.

PhD, Senior Research, Head of the problem research laboratory.

V.V. Semenets , Kharkiv National University of Radio Electronics, Kharkiv, Ukraine.

Dr Sc., Professor, Rector.

Qiang Guo, Harbin Engineering University

PhD, Professor, Director of Master and Ph. D Management Office, College of Information and Telecommunication.

Yu Zheng, Qingdao University, The People Republic of China.

PhD, Professor, Chair of Department Micro- and Nano- Electronics.

References

Weber C., Peter M., Felhauer T. Automatic modulation classification technique for radio monitoring, Electronics Letters, 2015, Vol. 51, Issue 10, pp. 794–796. DOI: 10.1049/el.2015.0610

Huang Yingkun, Weidong Jin, Bing Li, PengGe, Yunpu Wu Automatic Modulation Recognition of Radar Signals, Based on Manhattan Distance-Based Features, Access IEEE, 2019, Vol. 7, P. 41193–41204. DOI: 10.3724/sp.j.1087.2011.01730

Nandi A. K., Azzouz E. E. Automatic analogue modulation recognition, Signal Process, 1995, Vol. 46, No. 2, pp. 211–222. DOI: 10.15587/1729-4061.2019.176783

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

Li Dongjin, Ruijuan Yang, Xiaobai Li, Shengkun Zhu Radar Signal Modulation Recognition Based on Deep Joint Learning, IEEE Access, 2020, Vol. 8, pp. 48515–48528. DOI: 10.1109/access.2020.2978875

Yuanzeng Cheng, Zhang Hailong, Wang Yu Research on modulation recognition of the communication signal based on statistical model, 3rd 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, 9th International Conference on Intelligent Transport Systems Telecommunications, 2009, pp. 234–238. DOI: 10.1109/ITST.2009.5399351

Watanabe S. Methodologies of pattern recognition, Academic Press. Honolulu, University of Hawaii, 1969, 590 p. DOI:10.1016/C2013-0-12340-9

Duda R. O., Hart P. E., Stork D. G. Pattern classification 2nd Edition. New York, John Wiley & Sons, 2001, 654 p. DOI: 10.1007/s00357-007-0015-9

Hau C. C. Handbook of pattern recognition and computer vision. World Scientific, 2016, 584 p. DOI: 10.1142/9503

Bezruk V. M., Pevtsov G. V. Theoretical foundations of designing signal recognition systems for automated radio monitoring. Harkov, Kollegium, 2006, 430 p.

Bezruk V. М., Kaliuznyi N. М., Qiang Guo, Zheng Yu, Nikolaev I. М. Selection and recognition of the specified radio emissions based on the autoregression signal model, Radio Electronics, Computer Science, Control, 2020, No. 2, pp. 7–14. DOI: 10.15588/1607-3274-2020-2-1

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Published

2022-01-05

How to Cite

Bezruk , V., Kaliuzhnyi, M. M., Semenets , V., Guo, Q., & Zheng, Y. (2022). RECOGNITION METHOD OF SPECIFIED TYPES OF SIGNAL MODULATION BASED ON A PROBABILISTIC MODEL IN THE FORM OF A MIXTURE OF DISTRIBUTIONS. Radio Electronics, Computer Science, Control, (4), 7–14. https://doi.org/10.15588/1607-3274-2021-4-1

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Section

Radio electronics and telecommunications