Aircraft trajectory clustering techniques using circular statistics
Date
2016
Authors
McFadyen, A.
O'Flynn, M.
Martin, T.
Campbell, D.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
IEEE Aerospace Conference Proceedings, 2016, pp.1-10
Statement of Responsibility
Conference Name
IEEE Aerospace Conference (5 Mar 2016 - 12 Mar 2016 : Big Sky, US)
Abstract
This paper presents a statistical aircraft trajectory clustering approach aimed at discriminating between typical manned and expected unmanned traffic patterns. First, the track angle history for each trajectory is re-sampled and modelled using a mixture of Von Mises distributions (circular statistics). Second, the re-modelled trajectories are globally aligned using tools from bio-informatics. Third, the alignment scores are used to cluster the trajectories using an iterative k-medoids approach and an appropriate distance function. The approach is then evaluated using synthetically generated unmanned aircraft flights combined with real air traffic position reports taken over a sector of Northern Queensland, Australia. Results suggest that the technique is useful in distinguishing between expected unmanned and manned aircraft traffic behaviour, as well as identifying some common conventional air traffic patterns.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2016 IEEE