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

TL;DR

This paper presents a statistical methodology for characterizing aircraft approach and departure trajectories using clustering and Gaussian Processes to enhance airport security planning against terrorist threats.

Contribution

It introduces a novel two-step approach combining clustering and Gaussian Processes to model and select critical trajectories for airport defense strategies.

Findings

99.8% of the footprint underestimates less than 5% when using representative trajectories.

Method effectively captures trajectory deviations for strategic planning.

Approach enables automatic generation of vulnerable airspace regions.

Abstract

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.