The Effect of Rate-of-Climb Uncertainty on an Adaptive Trajectory Prediction Algorithm for Climbing Flights

Abstract

Previous research on an adaptive algorithm for climbing flights improved both trajectory prediction accuracy and conflict detection performance in fast-time simulations with rate-ofclimb data that were more precise than in actual operations. This is important because climb trajectories are difficult to predict, and large errors in these predictions reduce the potential operational benefits of some advanced concepts in the Next Generation Air Transportation System. This paper evaluates this algorithm under more realistic conditions with rate-ofclimb data that are only as precise as in the current system. In simulations with uncertainties in aircraft weight and rate of climb on the order of what are observed in actual operations, the algorithm lowered the altitude root mean square error by as much as 60%. While this is less than the 80% reduction achieved with precise rate-of-climb data, it is still a substantial improvement. The algorithm also decreased the missed-alert rate by 55% and 75% and the false-alert rate by 30% and 45%, respectively, with and without rate-of-climb uncertainties.