Method of Automatic Modulation Parameters Estimation of Short Duration Radio Signals With Binary Frequency Shift Keying




radio signal, frequency shift keying, symbol rate, symbol period, deviation, spectrum, periodogram, automation, instantaneous frequency


Method of automatically determining the carrier frequency, symbol rate, subcarrier separation frequency, manipulation index, and spectrum width of short duration radio signals with binary frequency manipulation are proposed in the article. The method combines four methods of frequency manipulation radio signals parameters estimation. To estimate the carrier frequency and the subcarrier spacing frequency, two well-known methods are used, which are based on the analysis of the characteristics of the amplitude-frequency spectrum of the radio signal and the instantaneous frequency histogram. To estimate the symbol rate, the method based on the analysis of the cyclostationary properties of the random function of the instantaneous frequency has been improved, and a new method based on the analysis of the peak values of the periodogram has been proposed. The improvement of the first method of symbol rate estimation consists in increasing the probability of correct identification of dominant harmonics in the amplitude-frequency spectrum of the centered normalized instantaneous frequency by preliminary low-pass filtering of the instantaneous frequency, increasing the dimension of the array of its values, and limiting the search band. The second method of symbol rate estimation is based on the search for peak values of the signal periodogram, which arise due to the presence of sections with alternating ones and zeros in the modulation bit sequences. It is shown that the analyzed characteristics have peak values whose frequencies (or frequency difference) are multiples of the symbol rate and frequency deviation. An approach to calculating parameters of frequency shift keying by combining data obtained in four ways is proposed. The efficiency of the method was verified by analyzing the radio signals obtained in the process of computer simulation, as well as real signals of command-telemetry radio lines of the unmanned aerial vehicle.

Author Biography

O. A. Nahorniuk , Zhytomyr Military Institute of S. P. Korolev, Zhytomyr, Ukraine

Candidate of Technical Sciences, Head of the Research Department of the Scientific Center



Yerilkin, A., Huriev, D., Karlov, D., Korobetskiy, O. and Shevchenko, Y. (2022). Review and analysis of world experience struggles with strike unmanned aviation. Science and Technology of the Air Force of Ukraine, Vol. 4, Iss. 49, pp. 15-22. doi: 10.30748/nitps.2022.49.02.

Su W., Kosinski J. (2003). Comparison and Simulation of Digital Modulation Recognition Algorithms. Proceedings of the International Symposium on Advanced Radio Technologies, pp. 81–87.

Technical identification of digital signals. Recommendation ITU-R SM.1600-3. Spectrum management (2017). ITU-R, Geneva, 32 p.

Nahorniuk O. A., and Pavliuk, V. V. (2016). Metodyka avtomatyzovanoho rozrakhunku parametriv chastotnoi manipuliatsii v umovakh apriornoi nevyznachenosti [Method of automated calculation of frequency shift keying parameters in conditions of a priori uncertainty]. Modern information technologies in the sphere of security and defence, Iss. 2(26), Kyiv, pp. 74–80.

Hui Xiong, De-Guo Zeng, Xiao-Dong He, Bin Tang (2010). Parameter Estimation Approach of FSK/PSK Radar Signal. Journal of Electronic Science and Technology, Vol. 8, Iss. 4, pp. 341-345. doi: 10.3969/j.issn.1674-862X.2010.04.009.

Grimaldi D., Palumbo A., Rapuano S. (2001). Automatic modulation classification and measurement of digitally modulated signals. 11 IMEKO TC-4 Symp. — Trends in Electrical Measurement and instrumentation, Lisbon, pp. 112–116.

Benvenuto N., Cherubini G., Tomasin S. (2021). Algorithms for Communications Systems and their Applications, 2nd edition. Wiley, 960 p.

Vito L., Dobre О. A. (2014). Joint Classification and Parameter Estimation of Compressive Sampled FSK Signals. 20th IMEKO TC4 International Symposium and 18th International Workshop on ADC Modelling and Testing, pp. 473–477.

Zhang J. et al. (2020). Blind Parameter Estimation of M-FSK Signals in the Presence of Alpha-Stable Noise. IEEE Transactions on Communications, Vol. 68, No. 12, pp. 7647-7659, doi: 10.1109/TCOMM.2020.3022348.

Jian-fei Xu, Fu-ping Wang, Zanji Wang (2011). Algorithm for symbol rate estimation of MFSK. International Conference on Graphic and Image Processing (ICGIP 2011), Vol. 8285, doi:10.1117/12.913362.

Valieva I., Björkman M., Åkerberg J., Ekström M., Voitenko I. (2022). Blind symbol rate estimation for cognitive radio using wavelet transform and deep learning for FSK modulated digital signals. International Conference on Advanced Technologies for Communications.

Sergienko A. (2012). Digital Communications, Saint Petersburg, 164 p.

Lyons, R. G. (2011). Understanding digital signal processing, 3d ed., Prentice Hall, Boston, 858 p.

Mikhalov G. A. (1999). Parametric estimates by the Monte Carlo method, Utrecht, 376 p.



How to Cite

Нагорнюк , О. А. (2023) “Method of Automatic Modulation Parameters Estimation of Short Duration Radio Signals With Binary Frequency Shift Keying”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (93), pp. 31-38. doi: 10.20535/RADAP.2023.93.31-38.



Telecommunication, navigation, radar systems, radiooptics and electroacoustics