Researchers at Tel Aviv University have unveiled a new technology aimed at enhancing drone detection capabilities, particularly in challenging weather conditions.
The system, developed by a team from the university's Faculty of Engineering, uses smart tagging and artificial intelligence to identify potentially hostile drones, even when visibility is poor.
"Mapping the airspace is critical to protecting the lives of soldiers and civilians," said Omer Tzadoki, a doctoral student involved in the project.
Traditional drone detection methods rely heavily on cameras, which can be ineffective in fog, cloud cover, or other adverse weather conditions. The new system addresses this limitation by employing radar technology coupled with an AI algorithm.
The AI analyzes electromagnetic radiation emitted by drones, effectively creating an electromagnetic "ID card" for each aircraft. This allows the system to distinguish between different types of drones and potentially identify friend from foe.
Professor Pavel Ginzburg, who led the research, emphasized the significance of the development. "The very process of identifying any drone on radar is complex enough, and therefore the ability to identify a specific drone is an achievement we are very proud of," he said.
The research team conducted initial tests in controlled laboratory settings before moving to outdoor trials to simulate real-world conditions.
While the technology is still in the development stage, it represents a potential advancement in airspace security. The researchers note that such innovations are particularly relevant in the current geopolitical climate.
The project team included doctoral student Omer Tzadoki, postdoctoral fellow Dmytro Vovchuk, and Kalil Hayon, all from the Department of Electrical Engineering at Tel Aviv University.
As drone technology continues to evolve, so too do the methods for detecting and classifying these unmanned aircraft. This development from Tel Aviv University marks another step in the ongoing effort to secure airspace in an increasingly complex aerial environment.
* Ynet contributed to this article.
