Tran Huu Nghi1, A.S. Podstrigaev2, Nguyen Trong Nhan3, D.A. Ikonenko4

1–4 Saint Petersburg Electrotechnical University «LETI» (St. Petersburg, Russia)

1 huunghiht@gmail.com, 2 ap0d@ya.ru, 3 10th20th30th@gmail.com, 4 dan-ikonenko@mail.ru

In a complex signal environment, signal recognition is one of the key stages of spectrum management. Spectrum management includes detecting and analyzing radio emissions to identify the sources of signals and interference, measuring the parameters of signals and interference, assessing their danger to the user, and determining the position of the sources of radio signals and interference on the ground. However, when conducting spectrum management, a priori information about the parameters of the received signal is usually not available. To process LFM signals with linear-decreasing and linear-increasing laws of frequency change, unmodulated radio pulses, and signals with binary and quadrature-phase manipulation earlier authors proposed an algorithm for signal recognition with detection at two intermediate frequencies. This algorithm is based on the use of FFT and relatively simple signal transformations, which allows us to implement it on commercially available FPGA and process the received signals in near real-time mode. To assess the reliability of recognition for the algorithm of recognition of signals with detection at two intermediate frequen-cies, the study of the probabilities of correct and erroneous recognition depends on the SNR is done. The justification of decision-making criteria for all possibilities of confusion in the recognition of signals and the computer modeling of the processing of the above signals in MATLAB environment have been completed. It is shown that the probability of correct recognition for all types of signals reaches at least 90% at an SNR equal to -2 dB. At the SNR of more than 0 dB, the probability of false recognition does not exceed 1 %, and the probability of correct recognition is at least 98%. Therefore, for practice, it is recommended to ensure that the input of the algorithm SNR is not less than -2...0 dB. With an SNR equal to 4 dB or more, the probability of correct recognition of all considered signal types is more than 99%, and the probability of misidentification is less than 1%. The new algorithm is compared with existing algorithms for recognizing signals of different types using FFT. It is shown that, in comparison with some algorithms, this algorithm allows to recognize more types of signals, and in comparison with others, it requires a smaller input SNR. The results obtained give grounds to recommend the algorithm of signal recognition with detection at two intermediate frequencies for use in spectrum management equipment for identification and separation of modern radiating radio-electronic means.

Tran Huu Nghi, Podstrigaev A.S., Nguyen Trong Nhan, Ikonenko D.A. Estimation of recognition confidence for the signal recognition algorithm with detection at two intermediate frequencies. Achievements of modern radioelectronics. 2023. V. 77. № 10. P. 70–79. DOI: https://doi.org/10.18127/j20700784-202310-07 [in Russian]

1. Rembovsky A., Ashikhmin A., Kozmin V., Smolskiy S. Radio monitoring: Problems, Methods and Equipment. Lecture notes in electrical engineering. Springer. 2009. DOI: https://doi.org/10.1007/978-0-387-98100-0.
2. Zhao Y., Luo X., Lin X., Wang H., Kui X., Zhou F., Wang J., Chen Y., Chen W. Visual analytics for electromagnetic situation awareness in radio monitoring and management. IEEE transactions on visualization and computer graphics. 2019. V. 26 (1). P. 590–600. DOI: 10.1109/TVCG.2019.2934655.
3. Dvornikov S.V., Dvornikov S.S., Pshenichnikov A.V. Apparat analiza chastotnogo resursa dlya rezhima psevdosluchaynoy perestroyki rabochey chastoty. Informatsionno-upravlyayushchie sistemy. 2019. № 4 (101). S. 62–68. DOI: 10.31799/1684-8853-2019-4-62-68. [in Russian]
4. Smirnov A.A., Kudryavtsev A.M., Zaika P.V. Model' informatsionnogo resursa avtomatizirovannogo kompleksa radiomonitoringa. Elektrosvyaz'. 2020. № 10. S. 42–48. DOI: 10.34832/ELSV.2020.11.10.006. [in Russian]
5. Manelis V.B., Koz'min V.A., Sladkikh V.A. Obnaruzhenie i identifikatsiya bazovykh stantsiy setey sotovoy svyazi 5G. Sistemy upravleniya, svyazi i bezopasnosti. 2021. № 3. S. 152–178. DOI: 10.24412/2410-9916-2021-3-152-178. [in Russian]
6. Nkhan N.Ch., Podstrigaev A.S., Leonov I.E. Matematicheskaya model' algoritma raspoznavaniya tipa modulyatsii signala v avtokorrelyatsionnom priemnike sredstv radiotekhnicheskogo monitoringa. Trudy MAI. 2020. № 113. S. 11. DOI: 10.34759/trd-2020-113-09. [in Russian]
7. Rembovskiy A.M., Sergienko A.R. Nosimye sredstva avtomatizirovannogo radiomonitoringa. Vremya. 2004. T. 5. № 6. S. 1x10-6. [in Russian]
8. Klyba N.S., Levsha A.V., Pechurin V.V., Shashlov V.A. Izmerenie urovnya pomekh na avtomatizirovannykh postakh radiomonitoringa. Naukoemkie tekhnologii. 2012. T. 13. № 8. S. 28–32. [in Russian]
9. Smolyakov A.V., Podstrigaev A.S. Eksperimental'noe issledovanie tochnosti opredeleniya chastotno-vremennykh parametrov impul'sa v tsifrovom priemnike s subdiskretizatsiey pri odnosignal'nom vozdeystvii. Trudy MAI. 2021. № 121. DOI: 10.34759/trd-2021-121-14. [in Russian]
10. Likhachev V.P., Podstrigaev A.S., Nhan N.T., Davydov V.V., Myazin N.S. Study of the accuracy of determining the location of radio emission sources with complex signals when using autocorrelation and matrix receivers in broadband tools for analyzing the electronic environment. Internet of Things, Smart Spaces, and Next Generation Networks and Systems. 2020. P. 326–333. DOI: https://doi.org/10.1007/978-3-030-65726-0_29.
11. Kuptsov V., Badenko V., Ivanov S., Fedotov A. Method for remote determination of object coordinates in space based on exact analytical solution of hyperbolic equations. Sensors. 2020. V. 20(19). P. 5472. DOI: https://doi.org/10.3390/s20195472. [in Russian]
12. Kuptsov V.D., Ivanov S.I., Fedotov A.A., Badenko V.L. High-precision analytical TDoA positioning algorithm for eliminating the ambiguity of coordinates determination. IOP Conference Series: Materials Science and Engineering. 2020. V. 904(1). P. 012013. DOI: 10.1088/1757-899X/904/1/012013.
13. Dyatlov A.P., Dyatlov P.A. Radiomonitoring. Spetsial'naya tekhnika. 2008. № 3–4. S. 4–8. [in Russian]
14. Podstrigaev A.S., Smolyakov A.V., Davydov V.V., Myazin N.S., Grebenikova N.M., Davydov R.V. New method for determining the probability of signals overlapping for the estimation of the stability of the radio monitoring systems in a complex signal environment. Internet of Things, Smart Spaces, and Next Generation Networks and Systems. 2019. P. 525–533. DOI: https://doi.org/10.1007/978-3-030-30859-9_45.
15. Likhachev V.P., Kupryashkin I.F., Semenov V.V., Sotnikov I.M. Obosnovanie trebovaniy k vychislitel'nomu ustroystvu tsifrovogo avtokorrelyatsionnogo priemnika signalov RSA. Zhurnal radioelektroniki. 2014. № 1. S. 9–9. [in Russian]
16. Dyatlov A.P., Dyatlov P.A., Kul'bikayan B.Kh. Radiomonitoring slozhnykh kvaziperiodicheskikh fazomanipulirovannykh signalov s neizvestnoy formoy. Izvestiya vysshikh uchebnykh zavedeniy. Severo-Kavkazskiy region. Estestvennye nauki. 2006. № 2. S. 45–49. [in Russian]
17. Podstrigaev A.S., Smolyakov A.V., Likhachev V.P. Programmno-opredelyaemye sredstva shirokopolosnogo analiza signalov na osnove tekhnologii subdiskretizatsii. SPb.: SPbGETU «LETI». 2021. [in Russian]
18. Dvornikov S.V., Saukov A.M. Metod raspoznavaniya radiosignalov na osnove veyvlet-paketov. Nauchnoe priborostroenie. 2004. T. 14. № 1. S. 85–93. [in Russian]
19. Patent № 2789386 C1. Sposob klassifikatsii signalov. Chan Kh.N., Podstrigaev A.S., Nguen Ch.N. Zayavl. 19.07.2022. Opubl. 02.02.2023. [in Russian]
20. Chan Khyu Ngkhi, Podstrigaev A.S., Nguen Chong Nkhan Algoritm klassifikatsii signalov s detektirovaniem na dvukh promezhutochnykh chastotakh dlya sredstv radiotekhnicheskogo monitoringa. Uspekhi sovremennoy radioelektroniki. 2022. T. 76. № 7. S. 30–39. DOI: https://doi.org/10.18127/j20700784-202207-03. [in Russian]
21. Tran H.N., Podstrigaev A.S., Trong N.N. A Signal Classification Algorithm with Detection at Two Intermediate Frequencies for RF Spectrum Monitoring. 2022 International Conference on Electrical Engineering and Photonics (EExPolytech). 2022. P. 91–94. DOI: 10.1109/EExPolytech56308.2022.9950890.
22. Galanina N.A., Ivanova N.N. Vychislitel'nye aspekty bystrogo preobrazovaniya Fur'e i voprosy ego realizatsii na PLIS. Vestnik Chuvashskogo universiteta. 2018. № 3. S. 172–181. [in Russian]
23. Saeed A., Elbably M., Abdelfadeel G., Eladawy M.I. Efficient FPGA implementation of FFT/IFFT processor. International Journal of circuits, systems and signal processing. 2009. V. 3 (3). P. 103–110.
24. Tsui J.B.Y. Special design topics in digital wideband receivers. Artech House. 2010.
25. Kubankova A. Design and analysis of new digital modulation classification method. WSEAS Transactions on Communications. 2009. V. 8 (7). P. 628–637.
26. Yang J., Wang X., Wu H. Modified automatic modulation recognition algorithm. 2009 5th International conference on wireless communications, networking and mobile computing. 2009. P. 1–4. DOI: 10.1109/WICOM.2009.5302483.
27. Zavadskiy A.L., Kazak P.A., Kadantsev S.M. Identifikatsiya vida modulyatsii fazomanipulirovannykh signalov na osnove analiza struktury spektra chetnykh stepeney. Tsifrovaya obrabotka signalov. 2019. № 1. S. 20–25. [in Russian]
28. Kubankova A., Kubanek D. Digital modulation recognition based on feature, spectrum and phase analysis and its testing with disturbed signals. 34th International Conference on TSP. 2011. P. 448–451.
29. Likhachev V.P., Semenov V.V., Veselkov A.A. Eksperimental'naya aprobatsiya algoritma opredeleniya chastotno-vremennykh parametrov LChM-signalov. Telekommunikatsii. 2016. № 5. S. 2–7. [in Russian]
30. Likhachev V.P., Kupryashkin I.F., Semenov V.V., Sotnikov I.M. Obosnovanie trebovaniy k vychislitel'nomu ustroystvu tsifrovogo avtokorrelyatsionnogo priemnika signalov RSA. Zhurnal radioelektroniki. 2014. № 1. S. 9. [in Russian]
31. Nkhan N.Ch., Podstrigaev A.S. Eksperimental'naya proverka algoritma raspoznavaniya tipa signala v avtokorrelyatsionnom priemnike. Vestnik Ryazanskogo gosudarstvennogo radiotekhnicheskogo universiteta. 2022. № 80. S. 46–52. DOI: 10.21667/1995-4565-2022-80-46-52. [in Russian]
32. Nhan N.T., Podstrigaev A.S., Nghi T.H. A Mathematical Model for Determining the Type of Signal Modulation in a Digital Receiver with Autocorrelation Processing. 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). 2021. P. 1650–1654. DOI: 10.1109/ElConRus51938.2021.9396097.
33. Patent № 2683791 C1. Sposob opredeleniya vidov radiolokatsionnykh signalov v avtokorrelyatsionnom priemnike. Likhachev V.P., Veselkov A.A., Nguen Ch.N. Zayavl. 09.04.2018. Opubl. 02.04.2019. [in Russian]
34. Nguen Ch. N., Podstrigaev A.S. Raspoznavanie signalov v avtokorrelyatsionnom priemnike radiotekhnicheskogo monitoringa. Vestnik svyazi. 2022. № 5. S. 36–40. [in Russian]
35. Likhachev V.P., Veselkov A.A., Nguen Ch.N. Kharakteristiki obnaruzheniya lineyno-chastotnomodulirovannykh, fazo-kodo-manipulirovannykh i prostykh radioimpul'sov v avtokorrelyatsionnom priemnike. Radiotekhnika. 2018. № 8. S. 71–76. DOI: 10.18127/j00338486-201808-14. [in Russian]