Hoverflies help to detect the sound of distant drones

Autonomous systems experts from the University of South Australia (UniSA), Flinders University and defence company Midspar Systems have reverse-engineered the visual systems of hoverflies to detect drones’ acoustic signatures from almost 4 km away, showing up to a 50% better detection rate than existing methods. Their findings, which could help combat the growing global threat posed by IED-carrying drones, have been reported in The Journal of the Acoustical Society of America.

UniSA Professor Anthony Finn said that insect vision systems have been mapped for some time now to improve camera-based detections, but this is the first time that bio-vision has been applied to acoustic data.

“Bio-vision processing has been shown to greatly increase the detection range of drones in both visual and infrared data,” Finn said. “However, we have now shown we can pick up clear and crisp acoustic signatures of drones, including very small and quiet ones, using an algorithm based on the hoverfly’s visual system.”

Researchers look for specific patterns (narrowband) and/or general signals (broadband) to pick up drone acoustics at short to medium distances, but at longer distance the signal is weaker and both techniques struggle to achieve reliable results. Similar conditions exist in the natural world; dark-lit regions are very noisy but insects such as the hoverfly have a very powerful visual system that can capture visual signals, researchers say.

“We worked under the assumption that the same processes which allow small visual targets to be seen amongst visual clutter could be redeployed to extract low-volume acoustic signatures from drones buried in noise,” said Dr Russell Brinkworth from Flinders University.

By converting acoustic signals into two-dimensional ‘images’ (called spectrograms), researchers used the neural pathway of the hoverfly brain to improve and suppress unrelated signals and noise, increasing the detection range for the sounds they wanted to detect. Compared with traditional techniques, bio-inspired processing improved detection ranges by between 30 and 49%, depending on the type of drone and the conditions.

The hoverfly’s superior visual and tracking skills have now been successfully modelled to detect drones in busy, complex and obscure landscapes, both for civilian and military purposes. Brinkworth said the ability to both see and hear small drones at greater distances could be hugely beneficial for aviation regulators, safety authorities and the wider public seeking to monitor ever increasing numbers of autonomous aircraft in sensitive airspace.

“We’ve witnessed drones entering airspace where commercial airlines are landing and taking off in recent years, so developing the capacity to actually monitor small drones when they’re active near our airports or in our skies could be extremely beneficial towards improving safety,” he said.

“The impact of UAVs in modern warfare is also becoming evident during the war in Ukraine, so keeping on top of their location is actually in the national interest. Our research aims to extend the detection range considerably as the use of drones increases in the civilian and military space.”

“Unauthorised drones pose distinctive threats to airports, individuals and military bases,” Finn added. “It is therefore becoming ever more critical for us to be able to detect specific locations of drones at long distances, using techniques that can pick up even the weakest signals. Our trials using the hoverfly-based algorithms show we can now do this.”

Image credit: ©stock.adobe.com/au/D.SERIN

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