Goals & Ambitions

As Unmanned Aerial Systems (UAS) or drones become more and more available, law enforcement agencies find themselves confronted with the novel task of having to police the access to the lower airspace. Commercial providers have already developed a wide range of solutions to this extent, but the capabilities of these systems are hard to benchmark. The result is that end-users have a hard time in matching the right tools to the specific use cases that they encounter.

To remedy this situation, the COURAGEOUS project will develop a standardized test methodology for detection, tracking and identification of nefarious drones’ utilising countermeasure systems to protect the lower airspace. This standardized test methodology will be based upon a series of standard user-defined scenarios representing a wide set of use cases (e.g. prison & airport security, critical infrastructure protection, border security, drugs & human trafficking, etc). For these scenarios, operational needs & functional performance requirements will be extracted by the COURAGEOUS end-users. Using this information, an integral test methodology will be developed that allows for a fair qualitative and quantitative comparison between different counter-UAS systems. This test methodology will be validated during three user-scripted validation trials, in Belgium, Greece and Spain.

In the short term, the COURAGEOUS standardized test methodology will lead to a much better understanding of the capabilities counter-UAS systems among law enforcement agencies, not only among COURAGEOUS partners, but also within the EU network of law enforcement agencies and even on a global scale, due to the extensive importance accorded in COURAGEOUS towards the widespread-yet-secure dissemination of project results, via partner INTERPOL.

In the medium-to-long term, an even more extensive set of commercial counter-UAS will be subjected to the COURAGEOUS test methodology, which will allow also developers of such systems to make design decisions based upon quantitative data.