This paper presents a comprehensive study of the use of software-defined radio (SDR) technology as an effective instrument for detecting radiofrequency activity associated with unmanned aerial vehicles (UAVs). The research is focused not only on the general principles of SDR but also on the practical implementation of algorithms and methods aimed at enhancing the efficiency of UAV signal detection in complex and dynamic radio environments. The architecture of SDR systems was analyzed in detail, highlighting their flexibility, scalability, and adaptability compared to conventional radio monitoring tools. Functional aspects, including wideband signal acquisition, reconfigurable software modules, and multi-channel processing capabilities, were examined as key enablers for UAV detection tasks. Special emphasis was placed on the development and application of mathematical tools and algorithms. Fourier transform, wavelet analysis, and power spectral density estimation were applied to perform spectral analysis and identify characteristic frequency components of UAV transmissions. Furthermore, methods for modulation classification and anomaly detection were implemented to distinguish UAV-related signals from background noise and interference. Synchronization techniques and preamble detection were tested to increase the reliability of identification under low signal-to-noise conditions. Based on the conducted analysis, a detection algorithm was designed and optimized. The algorithm takes into account noise resistance, multi-channel processing, and adaptive real-time data analysis. Its performance demonstrates the capability to reliably identify UAV communication signals even in the presence of intentional jamming and rapidly changing electromagnetic conditions. The results of this work create a methodological foundation for the design of advanced radiofrequency monitoring systems intended for electronic warfare and counter-UAV applications. The proposed SDR-based approach not only improves detection accuracy but also offers adaptability to future challenges in the field of UAV threat mitigation.
[1] J. Drozd, M. El-Absi, M. G. El-Soud, T. Z. Al-Soud, and P. Kvac, "Using SDR receivers for UAV detection," Elektronika ir Elektrotechnika, vol. 27, no. 4, pp. 31-37, 2021. DOI: 10.5755/j02.eie.29952
[2] E. P. de Freitas, H. F. de Arruda, F. A. A. de Souza, V. A. de Sousa, R. A. A. de Freitas, and G. A. de L. L. Ribeiro, "Experimental Evaluation of an SDR-Based UAV Localization System," Sensors, vol. 24, no. 9, p. 2789, 2024. DOI: 10.3390/s24092789
[3] A. Al-Hourani, S. Kandeepan, and S. Lardner, "RF-based drone detection and identification using deep learning," IEEE Transactions on Vehicular Technology, vol. 67, no. 5, pp. 3890-3902, 2018. DOI: 10.1109/TVT.2018.2792477
[4] F. C. Chiper, A. Martian, C. V. B. Vladescu, and I. A. Fagarasan, "Drone Detection and Defense Systems: Survey and a Software-Defined Radio-Based Solution," in 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 2021, pp. 1-6. DOI: 10.1109/ECAI52376.2021.9562854
[5] O. S. Al-Obaidi, A. J. Al-Ali, and T. K. Al-Uqaili, "A Survey on RF-based Drone Detection and Neutralization Techniques," in 2022 International Conference on Computer Science and Software Engineering (CSASE), 2022, pp. 1-6. DOI: 10.1109/CSASE51777.2022.9759495
[6] P. Flak, J. Lopatka, and P. Kawalec, "Drone Detection Sensor with Continuous 2.4 GHz ISM Band Coverage Based on Cost-Effective SDR Platform," IEEE Access, vol. 9, pp. 114674-114686, 2021. DOI: 10.1109/ACCESS.2021.3104738
[7] M. Ezuma, F. Erden, C. K. Anjinappa, O. Ozdemir, and I. Guvenc, "Micro-UAV detection and classification from RF fingerprints using deep learning," in 2019 IEEE Aerospace Conference, 2019, pp. 1-13. DOI: 10.1109/AERO.2019.8741749
[8] S. N. Al-Sadoon, A. G. Al-Suhail, and A. A. Al-Ali, "A Comprehensive Review of Drone Detection and Classification Methods," in 2023 International Conference on Communication, Computing, and Digital Systems (C-CODE), 2023, pp. 1-8. DOI: 10.1109/C-CODE58232.2023.10123164
[9] N. A. Al-Masri, M. S. Al-Issa, and M. I. Al-Hamar, "RF-Based UAV Detection and Identification Using Hierarchical Learning Approach," Sensors, vol. 21, no. 6, p. 1947, 2021. DOI: 10.3390/s21061947
[10] T. O'Shea, T. C. Clancy, and H. J. Ebeid, "Practical signal detection and classification in the RF spectrum," in 2016 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2016, pp. 1-10. DOI: 10.1109/DySPAN.2016.7543940
[11] S. Y. Zhuk, I. O. Tovkach, O. V. Neuimin, and V. V. Vasyliev, "Adaptive Filtering of UAV Movement Parameters Based on AOA-Measurements of the Sensor Network in the Presence of Abnormal Measurements," Journal of Aerospace Technology and Management, vol. 13, e4421, 2021. DOI: 10.5028/jatm.v13.1235
[12] V. M. Kartashov, V. M. Pososhenko, V. V. Voronin, V. V. Kolesnik, and O. V. Kapusta, "Methods for detection-recognition of radar, acoustic, optical and infrared signals of unmanned aerial vehicles," Радіотехніка, no. 205, pp. 138-153, 2021. DOI: 10.30837/rt.2021.2.205.15
[13] O. Yudin, R. Ziubina, S. Buchyk, and O. Matviichuk-Yudina, "Development of methods for identification of information-controlling signals of unmanned aircraft complex operator," Eastern-European Journal of Enterprise Technologies, vol. 2, no. 9 (104), pp. 26-36, 2020. DOI: 10.15587/1729-4061.2020.195510
[14] V. V. Tiutiunyk, O. A. Levterov, O. O. Tiutiunyk, and D. V. Usachov, "Acoustic monitoring of sources of emergency situations associated with the use of firearms," Проблеми надзвичайних ситуацій, vol. 2, no. 40, pp. 269–292, 2024. DOI: 10.52363/2524-0226-2024-40-19
[15] S. O. Sokolskyi and A. V. Movchanyuk, "Audio signal processing algorithm using machine learning method," Вісник НТУУ «КПІ». Серія Радіотехніка. Радіоапаратобудування, no. 93, pp. 39-51, 2023. DOI: 10.20535/RADAP.2023.93.39-51