Researchers from the NTI Competence Center focused on Geodata and Geoinformation Technologies at the Moscow State University of Geodesy and Cartography (MIIGAiK) have unveiled an artificial intelligence driven system capable of tracking as many as half a million unmanned aerial vehicles (UAVs) simultaneously in the airspace. This development was reported by RIA News, citing the NTI’s press service as the source.
The NTI press release notes that a functional prototype is slated for release by the end of 2023, with field tests planned for the following year. The new AI-based tracking framework is designed to handle large-scale UAV fleets, a step forward from current capabilities that manage far fewer platforms in real-time. Such a leap could transform how authorities coordinate airspace usage, ensure safety, and support complex missions that involve numerous flying assets across wide regions.
According to the briefing, coordinating drone operations presently involves numerous services to supervise and regulate the entire flight process. While the existing monitoring system can oversee around one hundred drones operating over a given area, expanding to several thousand vehicles presents significant operational and logistical challenges. The newer AI approach aims to streamline this supervision, reducing latency in decision making and improving the reliability of real-time situational awareness when drone deployments scale up dramatically.
Additional progress in unmanned technology comes from a separate initiative once associated with the Sakhalin Center for Unmanned Technologies, where a quadcopter design was developed to improve resilience against electronic warfare measures. This project underscores ongoing efforts to ensure UAVs maintain communication, navigation, and control under adverse conditions, which is critical for both civilian and defense-oriented applications.
Earlier reports also highlighted the involvement of researchers and engineers linked to the Bauman Moscow State Technical University, who contributed to a two-layer anti-icing coating for drones. The coating aims to increase flight safety by reducing ice buildup on critical surfaces, enabling more reliable operations in cold climates and challenging weather. Such material innovations complement software advances by enhancing the overall robustness of UAV platforms in varied environments.
In a broader context, Russia has pursued rapid drone detection capabilities as part of its evolving airspace security framework. The integration of fast-reacting detection systems with advanced AI-driven tracking could enhance both monitoring efficiency and response times, helping authorities detect intrusions, verify drone compliance with airspace rules, and coordinate appropriate countermeasures when necessary. Together, these threads illustrate a multi-layer approach to unmanned flight management, where sensors, algorithms, and hardened hardware work in concert to deliver safer, more capable drone operations across expansive regions.