Analysis of aircraft trajectories using Fourier descriptors and kernel density estimation

Handling the large amount of information from aircraft trajectories that are produced daily from air traffic control radar systems requires models for representing trajectories in a compact, easy to calculate, representative and distinctive form. These models should permit to perform clustering and classification operations efficiently and effectively. The Fourier descriptors have these characteristics and this article presents the results obtained on actual aircraft trajectories including approach and takeoff operations over a terminal area. Clustering and classification techniques in the feature space of Fourier descriptors were able to correctly separate the various types of operations. Additionally, based on the results of the clusters obtained, a method is presented for the classification of trajectories in progress based on kernel density estimation. An interesting result from the point of view of air traffic control for the detection of anomalous traffic is demonstrated.