Uncovering drone intentions using control physics informed machine learning
Adolfo Perrusquía,
Weisi Guo,
Benjamin Fraser
et al.
Abstract:Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across diverse application areas. Uncooperative drones do not announce their identity/flight plans and can pose a potential risk to critical infrastructures. Understanding drone’s intention is important to assigning risk and executing countermeasures. Intentions are often intangible and unobservable, and a variety of tangible intention classes are often inferred as a proxy. However, inference of drone intention classes using observational data a… Show more
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