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

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

V. М. Bezruk, N. М. Kaliuznyi, Guo Qiang, Yu Zheng, I. М. Nikolaev

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. 


Keywords


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

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References


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GOST Style Citations


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

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

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

4. Automatic modulation recognition of radar signals based on manhattan distance-based features / [Huang Yingkun, Jin Weidong, Li Bing et al.] // Access IEEE. – 2019. – Vol. 7. – Р. 41193–41204. DOI: 10.1109/ACCESS.2019.2907159

5. Zhilu Wu. Robust Automatic Modulation Classification Under Varying Noise Conditions / [Wu Zhilu, Zhou Siyang, Yin Zhendong et al.] // Access IEEE. – 2017. – Vol. 5. – Р. 19733–19741. DOI:10.1109/ACCESS.2017.2746140

6. Multi-Dimensional Wireless Signal Identification Based on Support Vector Machines / [Kürşat Tekbiyik, Özkan Akbunar, Ali Riza Ekti et al.] // Access IEEE. – 2019. – Vol. 7. – Р. 138890–138903. DOI 10.1109/ACCESS.2019.2942368

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

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

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

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

11. Hau C. C. Handbook of pattern recognition and computer vision. / C. C. Hau // World Scientific. – 2015. – Р. 584.

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

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

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

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

16. Detection and recognition of signals in HF radio monitoring / [V. Bezruk, O. Fedorov, S. Іvanenko et al.] // 19-th Conference on microwave techniques (COMITE-2019, Czech Republlic): proceedings. – 2019. – P. 293–297. DOI: 10.1109/RADIOELEK.2019.8733445

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

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

19. Selection and Recognition of Statistically Defined Signals in Learning Systems / [V. Bezruk, A. Omelchenko, O. Fedorov et al.] // 44-th Annual Conference of the IEEE Industrial Electronics Society (IECON-2018, USA) : proceedings. – 2018. – Р. 3211–3216. DOI:10.1109/IECON.2018.859132







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