Veracity Metrics for Ontological Decision-Making Support in Avionics Analytics

Air Traffic Management (ATM) is getting challenging mainly due to the massive information processed from increasingly sophisticated avionics systems, unprecedentedly crowded airspaces, and ever-changing weather conditions affecting grounded or flying aircraft. The complex situation overwhelms pilots and ATM controllers who work to keep aviation dependable. To provide artificial decision-making support for ATM and Unmanned Aerial System Traffic Management (UTM) systems, ontologies are an attractive knowledge technology. This paper proposes to endow an Avionics Analytics Ontology (AAO) with semantic uncertainty for input information to improve effectiveness in ATM/UTM decision-making processes. The approach is aligned with the Uncertainty Representation and Reasoning Evaluation Framework (URREF), that develops an uncertainty ontology. The degree of uncertainty is considered by means of metrics developed to quantify the information veracity. A case study is presented that involves two ATM/UTM operation scenarios where UAVs are flying nearby commercial aircraft and/or airports. It shows results from above application examples with source truth over 0.75 using Automatic Dependent Surveillance-Broadcast (ADSB) from London Heathrow Airport.