4-D Perturbation Analysis of Conflict Scenarios

Abstract

A method is presented for perturbing air traffic scenarios and analyzing the resulting conflicts. The perturbations consist of a specified range of spatial and temporal modifications of the trajectories, and the analysis identifies all possible conflicts within the perturbation range. This method enables new scenarios to be generated for simulation testing of air traffic management tools and concepts. Some potential applications are presented, such as the analysis of the sensitivity of a scenario to temporal perturbations, the prediction of areas of high-density traffic, and the estimation of conflict probabilities for long-range (one-to-six hour) trajectory predictions. A study of discrete and interpolated conflict detection accuracy as a function of the surveillance-sampling period is also presented, showing that interpolated detection halves the number missed conflicts when compare to discrete detection and that accuracy degrades significantly when the sampling period exceeds twelve seconds. I. Introduction To effectively cope with projected increased traffic demands on the National Airspace System (NAS), new operational concepts and tools are being researched and developed. The development must ensure that aircraft are separated at or above safe separation minima. When aircraft are predicted to violate the prescribed separation minima, they are said to be in conflict. Concepts that resolve conflicts and maintain safe separation are called separation assurance (SA) concepts. The effectiveness of SA concepts is evaluated through simulation using traffic scenarios that have conflicting flights. SA concepts need to be robust, so simulations need to be performed over a wide variety of scenarios containing diverse collections of conflicts. One can generate traffic scenarios containing conflicts from scratch, 1 but a more common practice is to generate scenarios by perturbing recorded scenarios derived from field observations or high-fidelity flight simulations. 2-4 This process shifts the original scenario trajectories in space and/or time to generate new scenarios with alternate sets of conflicts. These scenarios then drive simulations or exercise SA algorithms. Unfortunately, NAS simulations, especially ones involving live participants, can be extremely costly and time consuming. Therefore, efficiently identifying scenarios that have interesting or challenging sets of conflicts is desirable. Manual perturbations can be used to achieve this, but they risk not adequately representing the full range of potential scenarios. A more conservative approach is to generate a large number of randomly perturbed scenarios and then to evaluate them to detect conflicts. As the number of random perturbations increases, more potential conflicts are identified, but it is an extremely