Aircraft trajectory clustering techniques using circular statistics

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.