A new bio-inspired algorithm picks out the signal from the noise
In December 2018 thousands of holiday travelers were stranded at London's Gatwick Airport because of reports of drones flying nearby.
Unauthorized drones in commercial airspace have caused similar incidents in the U.S. and around the world.
To stop them, researchers are now developing a detection system inspired by a different type of airborne object: a living fly.
"It's quite awesome," says Frank Ruffier, a researcher at the Etienne-Jules Marey Institute of Movement Sciences at Aix-Marseille University in France and the French National Center for Scientific Research, who was not involved with the new study.
"This basic research on the fly is solving a real problem in computer science."
Now, with some help from nature, a team of scientists and engineers at the University of South Australia, the defense company Midspar Systems and Flinders University in Australia may have found a solution.
In their new paper, the scientists demonstrate that this combination can detect drones up to 50 percent farther away than conventional AI alone.
Instead of simply feeding visual data into the algorithm, the researchers fed it spectrograms-visual representations of sound-created from acoustic data recorded in an outdoor environment as drones flew by.
The algorithm was able to view these squiggly graphs and heighten the important "Signal" peaks that corresponded to frequencies emitted by drones.
At the same time, it was able to lessen the background noise that was not created by drones.