Method for Estimation the Number of Frequency Elements per Symbol for Signal with Fast Frequency Hopping and Frequency Manipulation

Authors

DOI:

https://doi.org/10.20535/RADAP.2021.84.48-56

Keywords:

information symbol, fast frequency hopping, frequency element, interpolation of spectral samples, frequency shift keying

Abstract

Introduction. Frequency hopping spread spectrum (FHSS) is widely used in modern digital communication systems to increase their noise immunity and intelligence protection. Radio systems using fast FHSS are characterized by a wide range of operating frequency, which leads a large number of interference at the input of the radio monitoring station, short duration of frequency elements, and division of information symbols into subsymbols transmitted at different frequencies.

Review of related works. The main trends in solving problem of estimating the parameters of FHSS in most publications are related to the use of time-frequency and wavelet analysis. But this methods and algorithms do not provide estimates of frequency elements number per information symbol for fast FHSS in case of interference.

Purpose and objectives of research. The purpose of this research is to develop an automated method for estimating the number of frequency elements of signals with fast FHSS per one information symbol with frequency shift keying.

Methodology of research. The proposed method consists of three stages: search for interference emissions and forming of notch filters; estimating of frequency elements; estimating the number of frequency elements per information symbol. Detection of interference emissions is realized by the time criterion. On the basis of the calculated frequency parameters of such radiations notch filters are formed. Window Fourier transform and discrete spectral interpolation were used to increase the accuracy of frequency element calculations. It is shown that to ensure the required accuracy of frequency calculation in a wide range of signal to noise ratio (SNR) values, it is advisable to use Gaussian spectral interpolation and Gaussian smoothing window with the parameter 0,3. Mathematical expressions are obtained for the probability of occurrence of a set of several frequency elements in a sequence, the difference between which is close to the step of the frequency grid. A criterion and algorithm for deciding on the number of frequency elements per one information symbol, based on statistical characteristics of the differences in the denominations of adjacent frequency elements was developed.

Simulation results. The efficiency and effectiveness of the developed method were tested by modeling wiht MATLAB software. The average probability of correctly estimating the number of frequency elements per symbol is not less than 0,9 at SNR above -10 dB.

Conclusions. Obtained probabilistic characteristics can be used in the development of methods for estimating other parameters of fast FHSS signals with frequency shift keying.

Author Biography

M. V. Buhaiov , Zhytomyr military institute named after S. P. Korolyov

Cand. Sci (Tech)

References

Перелік посилань

Torrieri D. Principles of spread-spectrum communication systems. 3rd ed. // Springer Science. — 2015. — 457 p. https://doi.org/10.1007/978-3-319-14096-4.

Chevva L., Sagar G. V. R. FH Signal Interception Based on the Time-Frequency Spectrogram by Image Enhancement Techniques // International Journal of Engineering Research and Applications. — 2012. — Vol. 2, Issue 2. — Р. 687−692.

Sha’ameri A. Z., Kanaa А. Robust multiple channel scanning and detection of low probability of intercept communication signals // Defense S&T technical bulletin. — STRIDE. — 2016. — Vol. 9, No. 1. — P. 1−17.

Pokrajac I. P. An algorithm for parameter estimation of frequency hopping emitters and their separation and grouping in unique radio networks / I. P. Pokrajac, M. Erić, M. L. Dukić // Scientific Technical Review. — 2004. — Vol. LIV, No. 3-4. — P. 15−23.

Draganić А, Orović І, Stanković S. FHSS Signal Characterization Based On The Cross-terms Free Time-Frequency Distributions // 2nd Mediterranean Conference on Embedded Computing MECO. — 2013. — 4 р.

Wan J., Zhang D., Xu W., Guo Q. Parameter Estimation of Multi Frequency Hopping Signals Based on Space-Time-Frequency Distribution // MDPI Symmetry. — 2019. — 18 р. DOI:10.3390/sym11050648.

Hamed H. A., Abdullah A. K., Al-waisawy S. Frequency Hopping Spread Spectrum Recognition Based on Discrete Transform and Skewness and Kurtosis // International Journal of Applied Engineering Research. — 2018. — Vol. 13, No. 9. — Р. 7081−7085.

Stevens D. L., Schuckers S. A. Low Probability of Intercept Frequency Hopping Signal Characterization Comparison using the Spectrogram and the Scalogram // Global Journal of Researches in Engineering. — 2016. — Vol. 16, Iss. 2. — Р. 13−23.

Lei Z, Yang P., Zheng L. Detection and Frequency Estimation of Frequency Hopping Spread Spectrum Signals Based on Channelized Modulated Wideband Converters // Electronics. — 2018. — Vol. 7, Iss. 9. — 18 p. DOI:10.3390/electronics7090170.

Бугайов М. В., Молодецький Б. В., Михайлюк І. О., Гордійчук В. В. Метод оцінювання параметрів сигналів радіостанцій зі швидкою псевдовипадковою перебудовою робочої частоти // Проблеми створення, випробування, застосування та експлуатації складних інформаційних систем : зб. наук. праць. Житомир: ЖВІ, 2019. — Вип. 17. — С. 14−26. DOI:10.46972/2076-1546.2019.17.02.

Нагорнюк О. А. Метод автоматичного визначення часових параметрів радіосигналів із псевдовипадковим перестроюванням робочої частоти на фоні вузькосмугових перешкод // Проблеми створення, випробування, застосування та експлуатації складних інформаційних систем: зб. наук. праць. Житомир: ЖВІ, 2018. — Вип. 15. — С. 53–64.

Макаренко С. И., Иванов М. С., Попов С. А. Помехозащищенность систем связи с псевдослучайной перестройкой рабочей частоты: Монография. // Санкт-Петербург: Свое изд-во, 2013. — 166 с.

Lyons R. G. Understanding digital signal processing, 3d ed. // Boston: Prentice Hall. — 2011. — 858 p.

Gasior M., Gonzalez J. L. Improving FFT frequency measurement resolution by parabolic and gaussian interpolation // Geneva, AIP Conference Proceedings. — 2004. — 18 р.

References

Torrieri D. (2015). Principles of Spread-Spectrum Communication Systems. 3rd ed., Springer Science, 457 p. DOI:10.1007/978-3-319-14096-4.

Chevva L., Sagar G. V. R. (2012). FH Signal Interception Based on the Time-Frequency Spectrogram by Image Enhancement Techniques. International Journal of Engineering Research and Applications, Vol. 2, Issue 2, pp. 687−692.

Sha’ameri A. Z., Kanaa А. (2016). Robust Multiple Channel Scanning and Detection of Low Probability of Intercept (LPI) Communication Signals. Defense S&T technical bulletin, STRIDE, Vol. 9, Num. 1, pp. 1−17.

Pokrajac I. P., Erić M., Dukić M. L. (2004). An algorithm for parameter estimation of frequency hopping emitters and their separation and grouping in unique radio networks. Scientific Technical Review, Vol. LIV, No. 3-4, pp. 15−23.

Draganić А, Orović І, Stanković S. (2013). FHSS Signal Characterization Based On The Cross-terms Free Time-Frequency Distributions. 2nd Mediterranean Conference on Embedded Computing MECO, 4 р.

Wan J., Zhang D., Xu W., Guo Q. (2019). Parameter Estimation of Multi Frequency Hopping Signals Based on Space-Time-Frequency Distribution. Symmetry, Vol. 11, Iss. 5. DOI:10.3390/sym11050648.

Hamed H. A., Abdullah A. K., Al-waisawy S. (2018). Frequency Hopping Spread Spectrum Recognition Based on Discrete Transform and Skewness and Kurtosis. International Journal of Applied Engineering Research, Vol. 13, No. 9, pp. 7081−7085.

Stevens D. L., Schuckers S. A. (2016). Low Probability of Intercept Frequency Hopping Signal Characterization Comparison using the Spectrogram and the Scalogram. Global Journal of Researches in Engineering, Vol. 16, Iss. 2, pp. 13−23.

Lei Z, Yang P., Zheng L. (2018). Detection and Frequency Estimation of Frequency Hopping Spread Spectrum Signals Based on Channelized Modulated Wideband Converters. Electronics, Vol. 7, Iss. 9, 18 p. DOI:10.3390/electronics7090170.

Buhaiov M. V., Molodetsky B. V., Muhailiuk I. O., Hordiychuk V. V. (2019). Metod otsiniuvannia parametriv syhnaliv radiostantsii zi shvydkoiu psevdovypadkovoiu perebudovoiu robochoi chastoty [Method of identification of radio stations with fast frequency hopping spread spectrum and frequency manipulation]. Problems of creation, testing, application and operation of complex information systems, ZVI, Zhytomyr, Vol. 2(17), pp. 14–26. DOI:10.46972/2076-1546.2019.17.02. [In Ukrainian].

Nahorniuk O. A. (2018). Method of automatic time parameters estimation of radio signals with frequency-hopping spread spectrum against the background of narrow-band interferences. Zbirnyk naukovykh prats ZhVI [Collection of scientific works of ZhVI], Zhytomyr, No. 15,pp. 53–64. [In Ukrainian].

Makarenko S. I., Ivanov M. S., Popov S. A. (2013). Pomekhozashchishchennost' sistem svyazi s psevdosluchainoi perestroikoi rabochei chastity [Noise Immunity of Communication Systems with Hopping Frequency ]. Svoe Izdatelstvo, Sankt-Peterburg, 166 p. [In Russian].

Lyons R. G. (2011). Understanding Digital Signal Processing, 3d ed. Boston: Prentice Hall, 858 p.

Gasior M., Gonzalez J. L. (2004). Improving FFT Frequency Measurement Resolution by Parabolic and Gaussian Spectrum Interpolation. AIP Conference Proceedings, Vol. 732, Iss. 1, 18 р. DOI:10.1063/1.1831158.

Published

2021-03-30

How to Cite

Нагорнюк , О. А. and Бугайов, М. В. (2021) “Method for Estimation the Number of Frequency Elements per Symbol for Signal with Fast Frequency Hopping and Frequency Manipulation”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (84), pp. 48-56. doi: 10.20535/RADAP.2021.84.48-56.

Issue

Section

Telecommunication, navigation, radar systems, radiooptics and electroacoustics