The aviation industry is undergoing a revolution with the adoption of GPS-based navigation and the integration of unmanned air traffic. These technological advances bring tremendous opportunities, but at the same time put many new challenges on the table for Air Traffic Management (ATM) and Air Traffic Control (ATC). These include for example the risk of spoofing and jamming, as well as (drone) penetrations in restricted airspaces or near incidents and even collisions.

Analysts now have access to a vast amount of location based data from ADS-B and other protocols broadcasting the location of both manned and unmanned air traffic. This data is available not only to ATC, but also for analysis, including throughput analysis, separation analysis, incident analysis, etc. Given the vast amount of data, a scalable tool such as is required to allow analysts to focus on their job and avoid going down the pitfall of having to spend numerous hours in reducing the data, implementing algorithms etc.

Researchers in the field of wireless communication and signal processing, are finding ways to counter spoofing and jamming, to increase the safety of our airspace. For example, the KU Leuven wireless research group of Dr. Pollin, is doing state-of-the-art research in detecting spoofing of ADS-B signals. Using AI-based algorithms, they develop robust methods that can tell whether the reported GPS location of an aircraft, is with high confidence a true location or a faked one. Researching such algorithms requires quick iteration to tune and tweak the parameters, and having a tool such as that allows quick API-based data ingestion and validation greatly improves their workflow.


The KU Leuven wireless communication group is using for quick data visualization and analysis in researching ADS-B anti-spoofing algorithms.