用声音信号检测无人机，还能识别无人机群。
Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks
- 用可解释的理性高斯小波提取声学特征，嵌入小型神经网络
- 室内和嘈杂室外环境均表现优于传统方法，可同时识别单机与群组
- 模型兼具高可解释性，适合安全监控与反无人机场景
无人飞行器（UAV）的检测对保护民用与军事设施至关重要。本文提出一种基于麦克风采集声音信号的低成本无人机检测系统。原始信号经由采用理性高斯小波的可解释自适应特征提取管道处理，这些小波变换与底层小型神经网络联合训练，实现基于特征的无人机检测与分类。该方法不仅可准确识别单架无人机，还可有效检测无人机群。我们在室内演播室及嘈杂户外环境中验证了性能，结果表明，该方法在单机与群组检测上均优于传统机器学习方法，同时保持高度可解释性。所提方法已开源，便于复现。
原文摘要 · Abstract (English)
The detection of unmanned aerial vehicles (UAVs) is important for the protection of civilian and military infrastructure. In this paper we propose a cost effective UAV detection system using sound signals obtained from microphones. The recorded signals are passed through a signal processing pipeline which employs interpretable adaptive feature extractors using so-called rational Gaussian wavelets. These adaptive wavelet transformations are embedded into and trained together with an underlying small neural network which detects and classifies UAVs based on the obtained features. This leads to a physically interpretable machine learning algorithm that in addition to classifying UAVs is also capable of detecting UAV swarms. We demonstrate our results using data collected in indoor studio and noisy outdoor environments. We conclude that the proposed method outperforms traditional machine learning approaches for detecting and classifying single UAVs as well as drone swarms, while retaining a high degree of interpretability. Our implementation of the proposed methods is made publicly available for reproducibility.
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