Causal probabilistic model for evaluating future transoceanic airline separations

With the advent of automatic dependent surveillance (ADS), a detailed probability model of aircraft cross-track deviations is required to determine the impact of ADS. A suitable causal probability model is presented where normal navigation, degradation, pilot blunder, and failure are each modeled by Gaussian density functions with mean and standard deviations defined by the physics of the event. The overall model is a weighted sum of the Gaussian error probabilities and is thus amenable to extrapolation. Overlap and encroachment probabilities are derived,and the effect of ADS on this is model determined. It is shown that by using a simple form of ADS separation standards can be reduced, and transmitting a figure of merit (FOM) providing information on failures and degradations can further reduce separation standards. The results suggest an improvement by a factor of two from current separation standards