Layered Counter-UAS Architecture with Open Standards

From detect-track-classify to defeat and forensic attribution: an open, AI-first approach.

Cheap drones have made air defence an economics problem. If each incoming airframe costs a few hundred pounds and each interceptor costs a few hundred thousand, you lose by winning.

That is the case for layers. Nothing in the chain is asked to do everything, and the expensive options are reserved for the targets that justify them.

Seeing and believing

Three-dimensional radar, radio-frequency geolocation and electro-optical or infrared sensors, with classifiers running on the sensor itself so the decision does not wait on a backhaul link. Fusion then has to resolve identity in heavy urban clutter, and it has to be able to explain its ranking. An operator who cannot see why a track is rated as hostile will not act on it.

Choosing an effect

Protocol takedown first, where the link can be taken rather than the aircraft. Resilience against GNSS spoofing, since the same trick works in both directions. Directed energy, including high-power microwave and laser systems such as DragonFire, for the cases where cost per shot is the deciding factor.

Afterwards

In home-base and critical national infrastructure settings the engagement is often the start of a legal process, not the end of an incident. Chain-of-custody logging produces an evidence package that will stand up in court. The wider posture sits inside the UK counter-uncrewed air systems strategy, with coalition interoperability proved through Federated Mission Networking.

The measures that matter are track purity in clutter, time to intercept under contested radio conditions, and the false positive rate at the chosen operating point. The last one is the one that gets systems switched off.