Itare Nathan, Thomas Jean-Hugh, Raoof Kosai, Blanchard Torea
Laboratoire d'Acoustique de l'Université du Mans (LAUM), UMR 6613, Institut d'Acoustique-Graduate School (IA-GS), CNRS, Le Mans Université, 72085 Le Mans, France.
Sensors (Basel). 2022 May 26;22(11):4021. doi: 10.3390/s22114021.
The development of unmanned aerial vehicles (UAVs) opens up a lot of opportunities but also brings some threats. Dealing with these threats is not easy and requires some good techniques. Knowing the location of the threat is essential to deal with an UAV that is displaying disturbing behavior. Many methods exist but can be very limited due to the size of UAVs or due to technological improvements over the years. However, the noise produced by the UAVs is still predominant, so it gives a good opening for the development of acoustic methods. The method presented here takes advantage of a microphone array with a processing based on time domain Delay and Sum Beamforming. In order to obtain a better signal to noise ratio, the UAV's acoustic signature is taken into account in the processing by using a time-frequency representation of the beamformer's output. Then, only the content related to this signature is considered to calculate the energy in one direction. This method enables to have a good robustness to noise and to localize an UAV with a poor spectral content or to separate two UAVs with different spectral contents. Simulation results and those of a real flight experiment are reported.
无人机（UAVs）的发展带来了诸多机遇，但也带来了一些威胁。应对这些威胁并非易事，需要一些良好的技术手段。了解威胁的位置对于应对表现出干扰行为的无人机至关重要。虽然存在许多方法，但由于无人机的尺寸或多年来的技术改进，这些方法可能非常有限。然而，无人机产生的噪音仍然占主导地位，因此这为声学方法的发展提供了良好契机。这里介绍的方法利用了一个麦克风阵列，并基于时域延迟求和波束形成进行处理。为了获得更好的信噪比，在处理过程中通过使用波束形成器输出的时频表示来考虑无人机的声学特征。然后，仅考虑与该特征相关的内容来计算一个方向上的能量。该方法能够对噪声具有良好的鲁棒性，并且能够定位频谱内容较差的无人机，或者分离具有不同频谱内容的两架无人机。报告了仿真结果和实际飞行实验结果。
