Air traffic management (ATM) is a vital service in air transportation provided by air traffic controllers (ATCs). Air traffic controllers and aircraft pilots are required to make increasingly difficult airspace decisions based on an ever-increasing number of information inputs, such as weather, flights, airspace, and reports. Additionally, proliferation of unmanned aerial systems raises concerns about safety and security (including cybersecurity) that challenge future airspace control. Additionally, ATM decision making requires dynamic situational awareness provided by mental cognitive faculties of airspace personnel. This paper presents a situation awareness (SAW) decision support system ATM reasoner (SAWDAR) approach to support decisional processes in ATM. The SAWDAR approach is based on artificial cognition implemented by means of knowledge representation (ontologies) and semantic reasoning. The ontological SAWDAR approach incorporates uncertainty considerations by including metrics for the veracity of surveillance systems, such as radars and automatic dependent surveillance-broadcast. By using the ontological SAW, experimental results demonstrate enhanced decision performance on airspace situations in a scenario, where an airplane is about to take off in the presence of potential hazardous drones flying around. The results demonstrate that the ontological SAWDAR approach can autonomously and dynamically support stakeholders (ATCs and pilots) to decide whether or not to allow aircraft takeoff. Concluding remarks and future research directions of avionics analytics ontology are also presented toward Next Generation Air Transportation System flight management.
Situation Awareness Decision Support System for Air Traffic Management Using Ontological Reasoning
Journal of Aerospace Information Systems ; 19 , 3 ; 224-245
2021-12-27
22 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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