The use of Global Navigation Satellite Systems (GNSS) is ubiquitous in civilian, security, and defense applications. The threat of a potential disruption, or even malicious supplantment, of GNSS is real and can lead to catastrophic consequences. Therefore, there is a growing need for protecting GNSS against intentional and unintentional interference sources. This project proposes an overarching collaborative framework to address interference management challenges which, in turn, preserve privacy of collaborating users who do not need to disclose their locations in the process. The project leverages massive data from users sharing an area, such as a smart city and uses AI models capable of learning complex behaviors of the various types of interferences. Federated learning schemes, which are privacy-preserving by design, are also employed in order to collectively estimate the threat probability over space and to localize interference sources.

2024

2025