Cognitive Decision Support System for Avionics Analytics

The combination of information from different sources is increasingly critical for Air Traffic Management (ATM) systems. It is not just about providing decision-making functional support to pilots, controllers, and unmanned aircraft but also dealing with non-functional ATM aspects such as cybersecurity. The use of knowledge representation through an Avionics Analytics Ontology (AAO) is an attractive solution to develop Decision Support Systems (DSSs) for ATM. This paper presents details of an AAO-based DSS prototype demonstrating the feasibility and performance of such a cognitive DSS. The AAO-DSS prototype is built of physics-based sensing, informatics-based processing, and human-derived communications for reporting, decisions, and actions. The prototype considers a case study which highlights benefits by means of simulating realistic airspace collision avoidance situations in downscaled scenarios, aircraft takeoffs and landing with drones flying nearby, and collision-avoidance airspace situations. This paper also shows preliminary performance analysis results (including information uncertainty estimates and cross-concept impacts in the AAO).