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New technology uses electromagnetic radiation scattered by drones as a means of identification.
By Pesach Benson, TPS
Israeli researchers unveiled a smart-tagging system to track and identify aerial drones in challenging conditions, such as urban environments, low-flight altitudes, and extreme weather.
Traditionally, drone identification relies on radars, cameras, and transponders, with the latter providing real-time location updates in civilian contexts.
However, these methods often falter in harsh conditions, such as limited visibility, crowded air traffic, and obstructive buildings that block satellite signals.
Reports of unauthorized drones entering restricted areas of airports have raised safety concerns.
In 2017, Ben Gurion International Airport was forced to shut down its airspace, suspending all arrivals and departures for 15 minutes when a civilian drone entered a prohibited area.
In 2015, an incoming airplane was forced to adjust its course to avoid a collision with a drone.
And the US Federal Aviation Administration reported in 2024 that it receives more than 100 such reports every month.
However new technology developed by Tel Aviv University aims to overcome these limitations by employing smart stickers and radar supported by an AI algorithm that classifies drones based on the electromagnetic radiation they scatter.
The smart-tagging technology was spearheaded by Ph.D. students Omer Tzidki and Dmytro Vovchuk under the guidance of Prof. Pavel Ginzburg.
“The simplest things often work best. This project leverages fundamental physical principles to reliably and accurately classify drones. The process of identifying any drone using radar is quite complex, so achieving the capability to identify specific drones is a significant accomplishment of which we are very proud,” said Ginzburg.
Tzidki explained that identifying drones becomes particularly critical in scenarios with no direct line of sight.
The new system achieves identification through an electromagnetic representation of the drone’s “identity card.”
This allows the radar to distinguish between drones with different IDs using electromagnetic tagging on the drone’s wings.
The AI algorithm, built on a neural network, classifies drones as either friendly or hostile, operating successfully even in harsh conditions and reducing the risk of accidents.
Initial experiments were conducted under controlled laboratory conditions before progressing to external trials to simulate real-world scenarios.
Tzidki stressed the combination of electromagnetic techniques, AI algorithms, and innovative radar technology to yield optimal results.
“Mapping the airfield is critical for protecting the lives of soldiers and civilians. This project is important at all times, and especially crucial now,” he said.
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