Framework Prototype to Test Decision Support System for Avionics Analytics

The use of ontologies is an attractive approach to develop systems to assist Air Traffic Controllers (ATCs) and pilots while making decisions, including complex airspace situations involving Unmanned Aerial Systems (UASs). Within the last decade, an Avionics Analytics Ontology (AAO) has been developed as a cognitive engine of a Decision Support System (DSS) for avionics analytics for application such as Air Traffic Management (ATM). The Federal Aviation Administration (FAA) Next Generation Air Transportation System (NextGen) and the Single European Sky ATM Research (SESAR) promote the use of ontologies to support avionics. However, just few research groups that support the FAA NextGen and the SESAR systems have reported on UAS cyber protection, including defense from drone swarms. This paper presents a framework prototype to test an AAO-based DSS for ATM (AODSATM). The AODSATM prototype is a proof-of-concept framework built of a software application and a hardware platform. The software part consists of the AAO, algorithms for data analytics, and routines for enterprise access. The hardware part consists of small aircraft required to emulate different roles such as a commercial airplane which is about to takeoff and drones like intruders in the airspace nearby the airplane. The challenge for the AAO-based DSS is to assist ATCs and pilots on what is the best course of action to do to avoid air collision when drones are flying around the aircraft. This paper also discusses design details of the DSS software application and shows test cases and a list of planned test procedures for the prototype trials. Concluding remarks and further steps for this research are also discussed.