ANALYSIS OF METHODS FOR DETECTING UNMANNED AERIAL VEHICLES
DOI:
https://doi.org/10.26906/SUNZ.2025.2.013Keywords:
unmanned aerial vehicle, UAV, detection, RF Spectrum Analysis, Real-Time Drone Monitoring, UAV classificationAbstract
In the modern era of increasing use of unmanned aerial vehicles (UAVs) in civilian, commercial, and military sectors, the issue of UAV detection has become critically important. Moreover, the development of artificial intelligence and machine learning algorithms opens new opportunities for improving identification accuracy and reducing false alarms. The use of advanced data processing methods allows detection systems to adapt to changing conditions and enhance their effectiveness in real-world environments. Therefore, research on UAV detection methods is relevant not only from a scientific perspective but also from a practical standpoint, as its findings can be applied to ensure security in urban areas, combat zones, and other critical fields. An unmanned aerial vehicle is an aircraft designed to operate without a pilot on board, with flight control carried out either by a pre-programmed system or via a remote control station located outside the aircraft. UAV detection is the process of locating and, in some cases, identifying an unmanned aerial vehicle. This article examines the primary UAV detection methods, analyzing their advantages and limitations. The study has demonstrated that none of the reviewed detection methods is universal. Each method has its own strengths and weaknesses, which can significantly impact the effectiveness of the system in real-world conditions. Based on the obtained results, promising directions for further research include: development of combined detection systems, application of deep learning methods, exploration of new physical principles for UAV detection. The results of this study lay the foundation for further advancement in UAV detection technologies, contributing to the development of more effective and adaptive security solutions.Downloads
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