A Hoverfly's Brain Serves as Inspiration for an Autonomous Sensor to Detect, Track Drones by Sound
Working on visual representations of incoming audio, this system can detect even small drones up to 50 percent further away than rivals.
A team of scientists from the University of South Australia and Flinders University, in partnership with Midspar Systems, have come up with a sensor capable of picking up distant drones by their sound alone — taking inspiration from the inner workings of a hoverfly's brain.
"Bio-vision processing has been shown to greatly increase the detection range of drones in both visual and infrared data," says Anthony Finn, professor of autonomous systems at the University of South Australia. "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."
The resulting bio-inspired audio sensor is capable, its creators claim, of boosting weak acoustic patterns and suppressing noise — increasing the detection range for small- and medium-sized uncrewed aerial vehicles by between 30 and 50 percent while also increasing flight parameter and trajectory calculations.
"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," explains co-author Russell Brinkworth, PhD and associate professor in autonomous systems at Flinders University.
The trick lies in converting the audio signals into spectrograms, two-dimensional images representing the incoming audio, which could then be processed by the system in the same way as vision passes through the neural pathway of the hoverfly's brain.
"Unauthorized drones pose distinctive threats to airports, individuals, and military bases," adds Finn. "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."
The team's work has been published in the Journal of the Acoustical Society of America, under open-access terms.