Acoustic detection of unmanned aerial vehicles using biologically inspired vision processing

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.& nbsp;VC2022 Author(s). All article content, except where otherwise noted, is licensed undera Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This study proposes and demonstrates a biologically inspired vision approach for acoustic detection of unmanned aerial vehicles. By learning from the early vision system of insects, it significantly enhances the signal-to-noise ratios in complex, cluttered, and low-light scenes, thereby achieving the enhancement of acoustic patterns and suppression of noise. Compared with traditional techniques, this method extends the detectable distance and improves the accuracy of flight parameter estimation for small and medium-sized unmanned aerial vehicles.