Probabilistic controlled airspace infringement tool

A current ground based safety net called Controlled Airspace infringement Tool (CAIT) is used by Air Traffic Controllers (ATCs). It warns them if any aircraft within uncontrolled airspace penetrates the Controlled airspace (CAS) without an advance clearance from the ATC. This penetration or `Infringement´ is considered as a major concern to ATCs where it may cause a possible conflict or mid-air collision. A conflict is an event which one aircraft loses its minimum separation to another. The downside of CAIT is that it only warns ATCs if the aircraft has already infringed CAS, this gives the ATC minimum time to react and avoid any conflict. In this research, we investigate a model which warns ATCs of possible future infringements accurately. We implement two Kalman filters (KF) as our tracking tool, one for each flight mode: constant velocity and constant acceleration each of which has its own state and observation errors. We will implement on-line learning too for these errors at each time step to predict the future uncertainties more accurately. We will then use two methods to estimate the probability of infringements: shortest distance and complement this method with the Monte Carlo sampling. We also used the switching Kalman filter (SKF) to choose between the most appropriate Kalman filter at each time step. On testing on real tracks, the SKF was found to give superior predictions of the aircraft location, permitting better estimates of the probability of CAS infringement to be made.