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Advances in UAV detection: integrating multi-sensor systems and AI for enhanced accuracy and efficiency

Author

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  • Semenyuk, Vladislav
  • Kurmashev, Ildar
  • Lupidi, Alberto
  • Alyoshin, Dmitriy
  • Kurmasheva, Liliya
  • Cantelli-Forti, Alessandro

Abstract

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.

Suggested Citation

  • Semenyuk, Vladislav & Kurmashev, Ildar & Lupidi, Alberto & Alyoshin, Dmitriy & Kurmasheva, Liliya & Cantelli-Forti, Alessandro, 2025. "Advances in UAV detection: integrating multi-sensor systems and AI for enhanced accuracy and efficiency," International Journal of Critical Infrastructure Protection, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:ijocip:v:49:y:2025:i:c:s1874548225000058
    DOI: 10.1016/j.ijcip.2025.100744
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    References listed on IDEAS

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    1. Yuan Wei & Tao Hong & Chaoqun Fang & Bo Rong, 2022. "Research on Information Fusion of Computer Vision and Radar Signals in UAV Target Identification," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-13, July.
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