Tejera-Berengue, D.; Zhu-Zhou, F.; Utrilla-Manso, M.; Gil-Pita, R.; Rosa-Zurera, M. Analysis of Distance and Environmental Impact on UAV Acoustic Detection. Electronics2024, 13, 643.
Tejera-Berengue, D.; Zhu-Zhou, F.; Utrilla-Manso, M.; Gil-Pita, R.; Rosa-Zurera, M. Analysis of Distance and Environmental Impact on UAV Acoustic Detection. Electronics 2024, 13, 643.
Tejera-Berengue, D.; Zhu-Zhou, F.; Utrilla-Manso, M.; Gil-Pita, R.; Rosa-Zurera, M. Analysis of Distance and Environmental Impact on UAV Acoustic Detection. Electronics2024, 13, 643.
Tejera-Berengue, D.; Zhu-Zhou, F.; Utrilla-Manso, M.; Gil-Pita, R.; Rosa-Zurera, M. Analysis of Distance and Environmental Impact on UAV Acoustic Detection. Electronics 2024, 13, 643.
Abstract
This study explores the dependence of drone acoustic detection systems performance on distance, using learning machines with different complexities, from simple linear discriminants, to deep neural networks, such as YAMNet, which exploits transfer learning. Other machine learning systems studied are the multilayer perceptron, support vector machines and random forest. These methods have been evaluated with a meticulously selected database that includes a wide variety of drone and interference sounds, previously processed by array signal processing and affected by ambient noise.
Two strategies for training learning machines are considered. In the first one, they are trained with unattenuated signals, trying to preserve the information generated by the sound sources, but they are tested considering the attenuation with distance, achieving effective detection at distances up to 200 metres with some methods, especially linear discriminant. In all cases, the existence of interferences that hinder detection is considered. In the second strategy, the systems are trained and tested with attenuated signals as a function of distance. The effective detection range increases up to 300 metres for most methods and up to 500 metres for the YAMNet based detector. In addition, this second approach raises the possibility of developing specialised detectors for each distance range, thus significantly extending the effective detection range. These results underline the promising potential of acoustic detection of drones at various distances, encouraging further exploration in this research area.
Keywords
UAV; drone; detection; distance; ROC; machine learning; transfer learning
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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