Scipediacontent: Scipediacontent moved page Draft Content 613058532 to McFadyen et al 2016a

2021-02-01T22:13:15Z

Scipediacontent moved page Draft Content 613058532 to McFadyen et al 2016a

Scipediacontent: Created page with " == Abstract == This paper presents a statistical aircraft trajectory clustering approach aimed at discriminating between typical manned and expected unmanned traffic pattern..."

2021-02-01T22:13:11Z

Created page with " == Abstract == This paper presents a statistical aircraft trajectory clustering approach aimed at discriminating between typical manned and expected unmanned traffic pattern..."

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== 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. Refereed/Peer-reviewed

== Original document ==

The different versions of the original document can be found in:

* [https://hdl.handle.net/11541.2/124190 https://hdl.handle.net/11541.2/124190]

* [http://xplorestaging.ieee.org/ielx7/7494113/7500496/07500601.pdf?arnumber=7500601 http://xplorestaging.ieee.org/ielx7/7494113/7500496/07500601.pdf?arnumber=7500601],
: [http://dx.doi.org/10.1109/aero.2016.7500601 http://dx.doi.org/10.1109/aero.2016.7500601]

* [https://eprints.qut.edu.au/94449 https://eprints.qut.edu.au/94449],
: [https://academic.microsoft.com/#/detail/2493154101 https://academic.microsoft.com/#/detail/2493154101]

* [ ]

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McFadyen et al 2016a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 613058532 to McFadyen et al 2016a

Scipediacontent: Created page with " == Abstract == This paper presents a statistical aircraft trajectory clustering approach aimed at discriminating between typical manned and expected unmanned traffic pattern..."