AI-based framework for drone intent classification using a non-cooperative radar system

With the proliferation of Unmanned Autonomous Vehicles (UAV) applications (e.g. surveying, medical deliveries etc), systems and services to allow them to co-exist with manned aviation and to be used within controlled airspace are being developed. To ensure safety, it is paramount that aviation users and operators are alerted of potential conflicts between aircraft and between aircraft and UAVs as well. In Europe, so-called 'U3 advanced services' are being developed that include capacity management and systems that support conflict detection and resolution specifically to enable UAV operations.

For the manned aviation case, a well-established concept known as Short Term Conflict Alert (STCA) is currently being used as a safety net to alert ATC operators of any situation where user defined minimum separation distances between any pair of cooperative surveillance (radar) tracks are predicted to be violated within a short look ahead time (usually 2 minutes). This is achieved via a visual alert on the radar display, though some systems also provide an audible alert. The system typically uses tracks based on cooperative radar, i.e. a system that acquires position information directly from a transponding aircraft. However, this solution cannot be applied to non-transponding air vehicles, and thus the future blended airspace of manned and unmanned air vehicles will need a more resilient and robust solution, to fully unlock the potential of UAV applications.

This PhD proposes to develop a robust conflict detection and alerting mechanism that is based on non-cooperative radar signatures to classify the aircraft intent, as an automated way to improve safety in a blended airspace, whilst meeting the false alerts rate requirements of manned aviation. The use of Metaheuristics and Deep Learning (DL) techniques in the classification of intent and subsequent detection of potential conflicts and their resolution will be investigated, in order to improve the conventional classification and conflict detection algorithms used. The solution will significantly enhance the capabilities of existing non-cooperative radar systems in manned aviation, as well as counter UAV radar systems, enhancing the safety and security of the blended airspace.