Preview

Journal of the Russian Universities. Radioelectronics

Advanced search

Parameter Justification of a Signal Recognition Algorithm Based on Detection at Two Intermediate Frequencies

https://doi.org/10.32603/1993-8985-2023-26-5-40-49

Abstract

   Introduction. The signal recognition task for the purposes of RF spectrum management can be solved using a signal recognition algorithm with detection at two intermediate frequencies. This algorithm is based on time–frequency analysis using fast Fourier transform (FFT) and signal envelope processing. Due to the relative simplicity of transformations, this algorithm is implemented on commercially available field programmable gate arrays and allows processing received signals in near real-time. However, the justification of the algorithm parameters providing effective signal recognition by the criterion of minimizing the signal-to-noise ratio (SNR) has not performed so far.

   Aim. Justification of parameters of the developed signal recognition algorithm, providing the minimum required SNR at the algorithm input.

   Materials and methods. The efficiency of the developed algorithm was estimated by computer simulation in the MATLAB environment.

   Results. The influence of the parameters of functional blocks and received signals on the efficiency of the developed algorithm was investigated. For chirp, simple pulse, binary, and quadrature phase shift keying signals, the following parameters are recommended: a pulse duration of 5…20 μs; a chirp rate of 0.8…24 MHz/μs; a code duration of 0.5…1 μs. For these signal parameters, the parameters of the algorithm ensuring its efficiency according to the given criterion are as follows: the number of FFT points equals 1024; the Hamming weight window; bandwidths of band-pass filters are 4 MHz; signal envelope amplitude averaging coefficient equals 0.15…0.25.

   Conclusion. The algorithm with the scientifically valid parameters can be used for recognition of signals at the input minimum SNR for the given types and parameters of signals.

About the Authors

Tran Huu Nghi
Saint Petersburg Electrotechnical University
Russian Federation

Tran Huu Nghi, Specialist in "Radioelectronic Systems and Complexes", Postgraduate Student. The author of 6 scientific publications

Department of Radio Electronic Means

Area of expertise: RF spectrum management

197022

5 F, Professor Popov St.

Saint Petersburg



A. S. Podstrigaev
Saint Petersburg Electrotechnical University
Russian Federation

Aleksey S. Podstrigaev, Cand. Sci. (2016), Associate Professor. The author of more than 120 scientific publications

Department of Radio Electronic Means

Area of expertise: design of complex radio systems; microwave devices; digital signal processing; wideband receivers

197022

5 F, Professor Popov St.

Saint Petersburg



Nguyen Trong Nhan
Le Quy Don Technical University
Viet Nam

Nguyen Trong Nhan, Cand. Sci. (2023), scientific collaborator. The author of more than 25 scientific publications

Area of expertise: radio engineering and telecommunications

Bac Tu Liem

236, Hoang Quoc Viet St.

Hanoi



D. A. Ikonenko
Saint Petersburg Electrotechnical University
Russian Federation

Danil A. Ikonenko, Master’s Student

Department of Computer Science and Engineering

Area of expertise: radio engineering and telecommunications

197022

5 F, Professor Popov St.

Saint Petersburg



References

1. Rembovsky A., Ashikhmin A., Kozmin V., Smolskiy S. Radio Monitoring: Problems, Methods and Equipment. Lecture notes in electrical engineering. Springer, 2009, 530 p. doi: 10.1007/978-0-387-98100-0

2. 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, vol. 20, no. 19, p. 5472. doi: 10.3390/s20195472

3. Nguyen T. N., Podstrigaev A. S., Leonov I. E. Mathematical Model of Signal Modulation Type Recognizing Algorithm in the Autocorrelation Receiver for Radio Engineering Monitoring Means. Trudy MAI. 2020, no. 113, p. 11. doi: 10.34759/trd-2020-113-09 (In Russ.)

4. Dvornikov S. V., Sivers M. A., Dvornikov A. S., Dvornikov S. S. Signal Recognition Based on the Probabilistic Evaluation of the Dispersion of Their Sign Vectors. Voprosy radioelektroniki, seriya Tekhnika televideniya [Issues of Radio Electronics, a Series of Television Technology]. 2020, no. 3, pp. 81–90. (In Russ.)

5. Utkin V. V., Korotkov V. A., Voynov D. C. Wavelet Filtration Application While Radio Technical Monitoring. The Herald of the Siberian State University of Telecommunications and Information Science. 2018, no. 1(41), pp. 64–71. (In Russ.)

6. Nikitin N. S., Darovskikh S. N. Synthesis of the Algorithm Identification of Signals with Linear Frequency Modulation. Bulletin of the Ural Federal District. Security in the Sphere of Information. 2019, no. 3(33), pp. 12–19. doi: 10.14529/secur190302 (In Russ.)

7. Lihachev V. P., Veselkov A. A., Nguen Ch. N. Ustroistvo dlya izmereniya kharakteristik sluchainykh protsessov [Method for Determining the Types of Radar Signals in an Autocorrelation Receiver]. Pat. RF, 2019, no. 2683791. (In Russ.)

8. Kubankova A. Design and Analysis of New Digital Modulation Classification Method. WSEAS Transactions on Communications. 2009, vol. 8, no. 7, pp. 628–637.

9. Yang J., Wang X., Wu H. Modified Automatic Modulation Recognition Algorithm. 2009 5<sup>th</sup> Intern. Conf. on Wireless Communications, Networking and Mobile Computing. Beijing, China, 24–26 Sept. 2009. IEEE, 2009, pp. 1–4. doi: 10.1109/WICOM.2009.5302483

10. Zavadsky A. L., Kazak P. A., Kadantsev S. M. Identification of the Modulation Type of Phase-Manipulated Signals Based on the Analysis of the Even Degree Spectrum Structure. Digital Signal Processing. 2019, no. 1, pp. 20–25. (in Russ.)

11. Kubankova A., Kubanek D. Digital Modulation Recognition Based on Feature, Spectrum and Phase Analysis and its Testing with Disturbed Signals. Recent Researches in Telecommunications, Informatics, Electronics and Signal Processing. 2011, pp. 162–166.

12. Tran H. N., Podstrigaev А., Trong N. N. A Signal Classification Algorithm with Detection at Two Intermediate Frequencies for RF Spectrum Monitoring. 2022 Intern. Conf. on Electrical Engineering and Photonics (EExPolytech). St. Petersburg, Russia, 20–21 Oct. 2022. IEEE, 2022, pp. 91–94. doi: 10.1109/EExPolytech56308.2022.9950890

13. Tran H. N., Podstrigaev А. S., Nguyen T. N. Sposob klassifikacii signalov [Signal Classification Method]. Pat. RF, no. 2789386, 2023. (In Russ.)

14. Prakasam P., Madheswaran M. Digital Modulation Identification Model Using Wavelet Transform and Statistical Parameters. J. of Computer Networks and Communications. 2008, vol. 2008, pp. 1–8. doi: 10.1155/2008/175236

15. Dvornikov S. V., Saukov A. M. Signal Identification Method Based on Wavelet-Packets. Nauchnoe Priborostroenie [Scientific Instrumentation]. 2004, vol. 14, no. 1, pp. 85–93. (In Russ.)

16. Stogov A. A., Tereshonok M. V., Chirov D. S., Kuzmin G. V. Modulation Type Recognition Using High Order Cumulants. T-Comm. 2012, no. 1, pp. 56–58. (In Russ.)

17. Swami A., Sadler B. M. Hierarchical Digital Modulation Classification Using Cumulants. IEEE Transactions on Communications. 2000, vol. 48, no. 3, pp. 416–429. doi: 10.1109/26.837045

18. Dobre O. A., Rajan S., Inkol R. Joint Signal Detection and Classification Based on First-Order Cyclostationarity for Cognitive Radios. EURASIP J. on Advances in Signal Processing. 2009, vol. 2009, pp. 1–12. doi: 10.1155/2009/656719

19. Chilukuri R. K., Kakarla H. K., Subbarao K. Estimation of Modulation Parameters of LPI Radar Using Cyclostationary Method. Sensing and Imaging. 2020, vol. 21, pp. 1–20. doi: 10.1007/s11220-020-00313-3

20. Milne P. R., Pace P. E. Wigner Distribution Detection and Analysis of FMCW and P-4 Polyphase LPI Waveforms. 2002 IEEE Intern. Conf. on Acoustics, Speech, and Signal Processing. Orlando, USA, 13–17 May 2002. IEEE, 2002, vol. 4, pp. IV-3944–IV-3947. doi: 10.1109/ICASSP.2002.5745520

21. Liu Y., Xiao P., Wu H., Xiao W. LPI Radar Signal Detection Based on Radial Integration of Choi-Williams Time-Frequency Image. J. of Systems Engineering and Electronics. 2015, vol. 26, no. 5, pp. 973–981. doi: 10.1109/JSEE.2015.00106

22. Adzhemov S. S., Tereshonok M. V., Chirov D. S. Type Recognition Of The Digital Modulation of Radio Signals Using Neural Networks. Moscow University Physics Bulletin. 2015, no. 1, pp. 23–28. (In Russ.)

23. Kim N., Kehtarnavaz N., Yeary M. B., Thornton S. DSP-Based Hierarchical Neural Network Modulation Signal Classification. IEEE Transactions on Neural Networks. 2003, vol. 14, no. 5, pp. 1065–1071. doi: 10.1109/TNN.2003.816037

24. Hazza A., Shoaib M., Alshebeili S. A., Fahad A. An Overview of Feature-Based Methods for Digital Modulation Classification. 2013 1<sup>st</sup> Intern. Conf. on Communications, Signal Processing, and Their Applications (ICCSPA). Sharjah, United Arab Emirates, 12–14 Febr. 2013. IEEE, 2013, pp. 1–6. doi: 10.1109/ICCSPA.2013.6487244

25. Sejdić E., Djurović I., Jiang J. Time–Frequency Feature Representation Using Energy Concentration: an Overview of Recent Advances. Digital Signal Processing. 2009, vol. 19, no. 1, pp. 153–183. doi: 10.1016/j.dsp.2007.12.004

26. Huang B., Jia G., Zhu Z. Multi-Channel RF Signal Analysis Device Based on FPGA and DSP. Sixth Intern. Conf. on Intelligent Computing, Communication, and Devices (ICCD 2023). 2023, vol. 12703, pp. 437–443. doi: 10.1117/12.2682801

27. Ivanova N., Galanina N., Moiseev D. FFT Algorithm Realization Features on FPGA. Bulletin of the Chuvash University. 2018, no. 3, pp. 182–191. (In Russ.)

28. Tsui J. B. Y. Special Design Topics in Digital Wideband Receivers. Artech House, 2010, 440 p.


Review

For citations:


Nghi T.H., Podstrigaev A.S., Nhan N.T., Ikonenko D.A. Parameter Justification of a Signal Recognition Algorithm Based on Detection at Two Intermediate Frequencies. Journal of the Russian Universities. Radioelectronics. 2023;26(5):40-49. https://doi.org/10.32603/1993-8985-2023-26-5-40-49

Views: 330


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1993-8985 (Print)
ISSN 2658-4794 (Online)