Genoud Adrien P, Saha Topu, Torsiello Joseph, Gatley Ian, Thomas Benjamin P
Department of Physics, New Jersey Institute of Technology, Newark, NJ 07102 USA.
Universite Claude Bernard Lyon 1, CNRS, Institut Lumière Matière, UMR5306, 69622 Villeurbanne, France.
Appl Phys B. 2025;131(8):171. doi: 10.1007/s00340-025-08533-9. Epub 2025 Aug 1.
The rapid proliferation of commercial unmanned aerial vehicles (UAVs) poses growing security, safety, and privacy challenges. This paper presents a novel frequency-domain analysis methodology to extract mechanical signatures of UAVs using backscattered optical signals from drone propellers. Through both simulations and experimental validation, the feasibility of retrieving key mechanical signatures, including the propeller's rotational speed (RPM) and the number of blades, was demonstrated. These signatures are a first step towards the real-time identification of drone models and provide insights into drone's flight behavior. The methodology, tested here with small toy drones, offers promise for real-world deployment of drone monitoring systems, complementing traditional detection techniques by operating in various atmospheric conditions. Additionally, harmonic and frequency peak analysis may allow for future improvements in trajectory tracking and payload detection. This work opens new possibilities for integrating lidar-based UAV characterization into both civilian and military airspace security frameworks.
商用无人机（UAV）的迅速普及带来了日益严峻的安全、安保及隐私挑战。本文提出了一种新颖的频域分析方法，利用无人机螺旋桨的反向散射光信号来提取无人机的机械特征。通过仿真和实验验证，证明了获取包括螺旋桨转速（RPM）和叶片数量在内的关键机械特征的可行性。这些特征是迈向实时识别无人机型号的第一步，并能深入了解无人机的飞行行为。这里用小型玩具无人机测试的该方法，有望在现实世界中部署无人机监测系统，通过在各种大气条件下运行来补充传统检测技术。此外，谐波和频率峰值分析可能会在未来改进轨迹跟踪和有效载荷检测。这项工作为将基于激光雷达的无人机特性分析集成到民用和军事空域安全框架中开辟了新的可能性。
