Weinert, A.; Underhill, N.; Serres, C.; Guendel, R. Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing. Aerospace2022, 9, 58.
Weinert, A.; Underhill, N.; Serres, C.; Guendel, R. Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing. Aerospace 2022, 9, 58.
Weinert, A.; Underhill, N.; Serres, C.; Guendel, R. Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing. Aerospace2022, 9, 58.
Weinert, A.; Underhill, N.; Serres, C.; Guendel, R. Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing. Aerospace 2022, 9, 58.
Abstract
The incorporation of unmanned aircraft terminal operations into the scope of Detect and Avoid systems necessitates analysis of the safety performance of those systems—principally, an assessment of how well those systems prevent loss of well clear from and collision with other aircraft. This type of analysis has typically been conducted by Monte Carlo simulation with synthetic, statistically representative encounters between aircraft drawn from an appropriate encounter model. While existing encounter models include terminal airspace classes, none explicitly represents the structure expected while engaged in terminal operations, e.g., aircraft in a traffic pattern. The work described herein is an initial model of such operations, scoped at this time specifically for assessment of unmanned aircraft landings and encounters with other aircraft either landing or taking off. The model shares the Bayesian network foundation of other MIT Lincoln Laboratory encounter models but tailors those networks to address structured terminal operations, i.e., correlations between trajectories and the airfield and each other. This initial model release is intended to elicit feedback from the standards-writing community.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
