Advances in UAV detection: integrating multi-sensor systems and AI for enhanced accuracy and efficiency
Semenyuk V. Kurmashev I. Lupidi A. Alyoshin D. Kurmasheva L. Cantelli-Forti A.
July 2025Elsevier B.V.
International Journal of Critical Infrastructure Protection
2025#49
This review critically examines the progress in unmanned aerial vehicle (UAV) detection and classification technologies from 2020 to the present. It highlights a range of detection methods, including radar, radio frequency (RF), optical, and acoustic sensors, with particular emphasis on the integration of these technologies through advanced sensor fusion techniques. The paper explores the core technologies driving improvements in detection accuracy, range, and reliability, with a special focus on the transformative role of artificial intelligence and machine learning. These innovations have significantly enhanced system performance, enabling more precise and efficient UAV detection. The review concludes with insights into emerging trends and future developments that promise to further refine UAV detection technologies, ensuring greater security and operational reliability.
Acoustic Sensor , Optical Sensor , Radar Technology , Sensor Fusion , UAV Classification , UAV Detection
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M. Kozybaev North Kazakhstan University, Pushkin street, 86, Petropavlovsk, 150000, Kazakhstan
RaSS (Radar and Surveillance Systems) National Laboratory, Pisa, 56124, Italy
M. Kozybaev North Kazakhstan University
RaSS (Radar and Surveillance Systems) National Laboratory
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026