Abstract
Avionics systems are getting increasingly sophisticated, airspaces are densely occupied, and aircraft are desired to fly in more adverse weather conditions. These conditions increase the complexity of Air Traffic Management (ATM) as aviators and airspace controllers struggle to maintain safety while cross-checking multisource information, including information from Unmanned Aerial Systems (UASs) . Hence, future ATM decision-support systems are required not only to be autonomous and reliable complex decision-making processes with minimal human intervention, but also must be able to deal with UAS ATM (UTM). This chapter presents the implementation of Ontologies for NextGen Avionics Systems (ONAS) for UTM. The ONAS approach consists of an operation framework and an ontology-based tool, called Avionics Analytics Ontology (AAO), to support decision-making in advanced ATM/UTM systems. The AAO entails a cognitive ATM/UTM architecture for avionics analytics where an ontological database captures information related to weather, flights, and airspace. The AAO-based decision-making process supports human Situation AWareness (SAW) as well as machine Situation Assessment (SA). The ONAS approach presented is intended to be initially used in civil aviation. A use case along with two different scenarios is presented for an ATM/UTM system. The scenarios represent realistic flight situations (based on dataset from a flight tracking service) where the ATM/UTM decisions made are supported by the AAO.
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Insaurralde, C.C., Blasch, E. (2018). Ontologies in Aeronautics. In: Durak, U., Becker, J., Hartmann, S., Voros, N. (eds) Advances in Aeronautical Informatics. Springer, Cham. https://doi.org/10.1007/978-3-319-75058-3_6
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