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Cluster-Based Flight Trajectory Outlier DetectionGiven a set of flight trajectories, can we classify the trajectories that do not follow a normal path? By identifying abnormal trajectories, further analysis can be done to determine the reasoning for these actions. Addressing these scenarios can bring possible solutions for holding and rerouting problems when its time to incorporate UAM in the airspace.
Document ID
20210020084
Acquisition Source
Ames Research Center
Document Type
Presentation
Authors
Mikol Forney
(Universities Space Research Association Columbia, Maryland, United States)
Banavar Sridhar
(Universities Space Research Association Columbia, Maryland, United States)
Kenneth Freeman
(Ames Research Center Mountain View, California, United States)
Date Acquired
August 5, 2021
Subject Category
Aeronautics (General)
Meeting Information
Meeting: Secure Airspace Intern Exit Presentations
Location: Virtual
Country: US
Start Date: August 13, 2021
End Date: August 13, 2021
Sponsors: Ames Research Center
Funding Number(s)
WBS: 128698
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Technical Management
Keywords
Machine Learning
Flight Trajectories
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