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

Applications