Causal probability model for transoceanic track separations with applications to automatic dependent surveillance

With the advent of automatic dependent surveillance (ADS), a detailed model of aircraft crosstrack deviations is required to determine the impact of ADS. The authors present a suitable probability model which is amenable to extrapolation. Normal navigation, degradation, pilot blunders, and failures are characterized by Gaussian density functions with associated standard deviations defined by the physics of the event. The overall model is a weighted sum of these Gaussian error probabilities. Overlap and encroachment probabilities are derived, and the impact of ADS on this model determined. It is shown that, by using the simplest form of ADS, the separation standards can be reduced and in addition, by transmitting a figure of merit (FOM) providing information on failures and degradations, the separation standards can be further reduced. The results suggest an improvement by a factor of two over current separation standards