用声音识别无人机送货，100米内准确率达96%
Acoustic Drone Package Delivery Detection
- 通过麦克风阵列分析声纹，从频谱中提取螺旋桨转速和投递时刻
- 150米内螺旋桨频率误差仅16赫兹，投递事件检测准确率96%
- 适合监狱等禁区安防，无需视觉设备，抗遮挡能力强
近年来，无人机在监狱等禁区内非法投送物品已成为重大安全挑战。尽管已有大量研究聚焦于无人机探测与定位，但针对投递行为的识别仍鲜有关注。本研究首次提出基于地面麦克风阵列的声学投递检测算法，仅依靠声学特征同时估计无人机螺旋桨转速与投递事件。采用深度神经网络从梅尔频谱中检测无人机存在并估计其桨叶通过频率（BPF）。算法通过分析BPF在特定时间前后突变情况，识别可能的投递时刻。结果显示，当无人机距离麦克风阵列小于150米时，桨叶通过频率估计的平均绝对误差为16赫兹；无人机存在检测准确率达97%；投递事件检测正确率为96%，误报率为8%。研究表明，利用声学信号可在100米范围内有效识别投递行为。
原文摘要 · Abstract (English)
In recent years, the illicit use of unmanned aerial vehicles (UAVs) for deliveries in restricted area such as prisons became a significant security challenge. While numerous studies have focused on UAV detection or localization, little attention has been given to delivery events identification. This study presents the first acoustic package delivery detection algorithm using a ground-based microphone array. The proposed method estimates both the drone's propeller speed and the delivery event using solely acoustic features. A deep neural network detects the presence of a drone and estimates the propeller's rotation speed or blade passing frequency (BPF) from a mel spectrogram. The algorithm analyzes the BPFs to identify probable delivery moments based on sudden changes before and after a specific time. Results demonstrate a mean absolute error of the blade passing frequency estimator of 16 Hz when the drone is less than 150 meters away from the microphone array. The drone presence detection estimator has a accuracy of 97%. The delivery detection algorithm correctly identifies 96% of events with a false positive rate of 8%. This study shows that deliveries can be identified using acoustic signals up to a range of 100 meters.
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