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Take-off and landing are the periods of a flight where aircraft are mostvulnerable to a ground based rocket attack by terrorists. While aircraftapproach and depart from airports on pre-defined flight paths, there is adegree of uncertainty in the trajectory of each individual aircraft. Capturingand characterizing these deviations is important for accurate strategicplanning for the defence of airports against terrorist attack. A methodology isdemonstrated whereby approach and departure trajectories to a given airport arecharacterized statistically from historical data. It uses a two-step process offirst clustering to extract the common trend, and then modelling uncertaintyusing Gaussian Processes (GPs). Furthermore it is shown that this approach canbe used to either select probabilistic regions of airspace where trajectoriesare likely and - if required - can automatically generate a set ofrepresentative trajectories, or select key trajectories that are both likelyand critically vulnerable. An evaluation of the methodology is demonstrated onan example data-set collected by the ground radar at an airport. The evaluationindicates that 99.8% of the calculated footprint underestimates less than 5%when replacing the original trajectory data with a set of representativetrajectories.

Trajectory Clustering, Modelling, and Selection with the Focus on Airspace Protection