Science, Technology, Engineering and Mathematics, University of South Australia, Adelaide, South Australia 5095, Australia.
Midspar Systems, Farrer Place, Oyster Bay, New South Wales 2225, Australia.
J Acoust Soc Am. 2022 Feb;151(2):968. doi: 10.1121/10.0009350.
Robust detection of acoustically quiet, slow-moving, small unmanned aerial vehicles is challenging. A biologically inspired vision approach applied to the acoustic detection of unmanned aerial vehicles is proposed and demonstrated. The early vision system of insects significantly enhances signal-to-noise ratios in complex, cluttered, and low-light (noisy) scenes. Traditional time-frequency analysis allows acoustic signals to be visualized as images using spectrograms and correlograms. The signals of interest in these representations of acoustic signals, such as linearly related harmonics or broadband correlation peaks, essentially offer equivalence to meaningful image patterns immersed in noise. By applying a model of the photoreceptor stage of the hoverfly vision system, it is shown that the acoustic patterns can be enhanced and noise greatly suppressed. Compared with traditional narrowband and broadband techniques, the bio-inspired processing can extend the maximum detectable distance of the small and medium-sized unmanned aerial vehicles by between 30% and 50%, while simultaneously increasing the accuracy of flight parameter and trajectory estimations.
稳健地检测静音、慢速、小型无人机具有挑战性。本文提出并展示了一种应用于无人机声学检测的受生物启发的视觉方法。昆虫的早期视觉系统极大地提高了复杂、杂乱和低光（嘈杂）场景中的信噪比。传统的时频分析允许使用声谱图和相关图将声学信号可视化为图像。在这些声学信号的表示中，感兴趣的信号，如线性相关的谐波或宽带相关峰，本质上提供了与噪声中沉浸的有意义的图像模式等效的信息。通过应用悬停蝇视觉系统的光感受器阶段模型，表明可以增强声学模式并大大抑制噪声。与传统的窄带和宽带技术相比，受生物启发的处理可以将中小型无人机的最大可检测距离延长 30%至 50%，同时提高飞行参数和轨迹估计的准确性。
