Acoustic Drone Detection for Technical Infrastructure Security and Defense Purposes
Jarosław ZIÓŁKOWSKI, Patrycja GUZANEK, Mateusz MAZUR, Izabella KĘSY, Robert SZCZUR
Abstract. With the widespread use of drones, new challenges have arisen in the field of technical security of facilitates and defense. This article discusses passive detection systems for unmanned aerial vehicles (UAVs) based on acoustic signatures. It presents an overview of methods for detecting drones using sound – from classical signal analysis and machine learning methods to modern deep learning algorithms – in the context of applications in critical infrastructure protection and military systems. Available solutions, their effectiveness and detection range were compared, and the difficulties of background noise and distinguishing between drone types were discussed. Research results were presented indicating high acoustic detection efficiency under favorable conditions (even above 90% using algorithms based on neural networks), as well as limitations caused by a decrease in signal-to-noise ratio with distance from the drone. The conclusion emphasizes the role of acoustic systems as a complement to other UAV detection techniques and their importance for maintaining safe operation of technical systems.
Keywords
Drone Detection, Acoustic Signature, Technical Safety, Background Noises
Published online 1/25/2026, 6 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Jarosław ZIÓŁKOWSKI, Patrycja GUZANEK, Mateusz MAZUR, Izabella KĘSY, Robert SZCZUR, Acoustic Drone Detection for Technical Infrastructure Security and Defense Purposes, Materials Research Proceedings, Vol. 62, pp 58-63, 2026
DOI: https://doi.org/10.21741/9781644904015-8
The article was published as article 8 of the book Terotechnology XIV
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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