M.V. Tsvetkov1

1 MIREA – Institute of Radio Electronics and Informatics (Moscow, Russia)
1 jpotemkina3@gmail.com

This article is dedicated to the study of applying artificial intelligence methods, particularly neural networks, for detecting control signals of unmanned aerial vehicles (UAVs) within the framework of electronic intelligence and counter-UAV tasks. The features of UAV signals are examined, such as the use of proprietary protocols, noise-resistant coding, and adaptive parameter adjustments depending on the quality of the communication channel. The limitations of traditional detection methods are analyzed, and the prospects for the neural network approach, capable of identifying hidden signal traits under interference conditions, are substantiated. A generalized methodology for neural network application is provided, including data collection and labeling, preprocessing, feature extraction, classifier training, and post-processing of results. Examples of successful application of various neural network architectures (convolutional, recurrent, ensemble) for detecting UAV signals under low signal-to-noise ratios (SNR) are demonstrated. Special attention is given to the principles of convolutional networks working with signal spectrograms, enabling the detection of characteristic frequency-temporal patterns of UAV signals. In conclusion, promising research directions are discussed, aimed at optimizing neural network architectures, expanding training datasets, adapting to new protocols, improving interpretability, integrating into electronic warfare systems, and mitigating electronic suppression methods.

Tsvetkov M.V. Application of deep learning for real-time identification of drone control patterns using distributed sensor networks. Achievements of modern radioelectronics. 2025. V. 79. № 9. P. 59–69. DOI: https://doi.org/10.18127/ j20700784-202509-08 [in Russian]

1. Ahmed A., Quoitin B., Gros A, Moeyaert V. A Comprehensive Survey on Deep Learning-Based LoRa Radio Frequency Fingerprinting Identification. Sensors (Basel). 2024 Jul 8. V. 24(13). P. 4411. DOI: 10.3390/s24134411. PMID: 39001190; PMCID: PMC11244599.
2. Jagannath A., Jagannath J., Kumar P.S.P.V. A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges. Comput. Netw. 2022. V. 219. P. 109455. DOI: 10.1016/j.comnet.2022.109455.
3. Jouhari M., Saeed N., Alouini M.S., Amhoud E.M. A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges. IEEE Commun. Surv. Tutor. 2023. V. 25. P. 1841–1876. DOI: 10.1109/COMST.2023.3274934.
4. Khalifeh A., Aldahdouh K.A., Darabkh K.A., Al-Sit W. A Survey of 5G Emerging Wireless Technologies Featuring LoRaWAN, Sigfox, NB-IoT and LTE-M. Proceedings of the 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). Chennai, India. 21–23 March 2019. P. 561–566.
5. Krejčí R., Hujňák O., Švepeš M. Security survey of the IoT wireless protocols. Proceedings of the 2017 25th Telecommunication Forum (TELFOR). Belgrade, Serbia. 21–22 November 2017. P. 1–4.
6. Padiya S.D., Gulhane V.S. IoT and BLE Beacons: Demand, Challenges, Requirements, and Research Opportunities- Planning-Strategy. Proceedings of the 2020 IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT). Gwalior, India. 10–12 April 2020. P. 125–129.
7. Sung W.J., Ahn H.G., Kim J.B., Choi S.G. Protecting end-device from replay attack on LoRaWAN. Proceedings of the 2018 20th International Conference on Advanced Communication Technology (ICACT). Chuncheon, Republic of Korea. 11–14 February 2018. P. 167–171.
8. Xie L., Peng L., Zhang J., Hu A. Radio frequency fingerprint identification for Internet of Things. A survey. Secur. Saf. 2024. V. 3. P. 2023022. DOI: 10.1051/sands/2023022.
9. Tsvetnov V.V., Demin V.P., Kupriyanov A.I. Radioelektronnaya bor'ba. Radiorazvedka i radioprotivodejstvie. M.: MAI. 1998. 248 s. [in Russian].
10. Kupriyanov A.I., Shustov L.N. Radioelektronnaya bor'ba. Osnovy teorii. M.: Vuzovskaya kniga. 2017. 800 s. [in Russian].
11. Palij A.I. Ocherki istorii radioelektronnoj bor'by. M.: Vuzovskaya kniga. 2006. 304 s. [in Russian].
12. Sozykin A.V. Obzor metodov obucheniya glubokih nejronnyh setej. Vestnik YuUrGU. Seriya: Vychislitel'naya matematika i informatika. 2017. T. 6. № 3. S. 28–59 [in Russian].
13. Ignatenko N.V., Polikanin A.N. Metody protivodejstviya razvedke s ispol'zovaniem malogabaritnyh bespilotnyh letatel'nyh apparatov. Vestnik SGUGiT. 2018. T. 23. № 4. S. 187–196 [in Russian].
14. Velikodnev A.S., Tret'yakov I.D., Shurgin V.A. Radioelektronnaya razvedka i radioelektronnaya bor'ba. Osobennosti razvedki i protivodejstviya. Molodoj uchenyj. 2023. № 45 (492). S. 3–6 [in Russian].