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Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing

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Author(s)
Weinert, Andrew
•
Underhill, Ngaire
•
Serres, Christine
•
Guendel, Randal
Date Issued
January 24, 2022
Journal
Aerospace
Publisher
Multidisciplinary Digital Publishing Institute
Citation
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 9 (2): 58 (2022)
Version
Final published version
Abstract
The integration of new airspace entrants into terminal operations requires design and evaluation of Detect and Avoid systems that prevent loss of well clear from and collision with other aircraft. Prior to standardization or deployment, an analysis of the safety performance of those systems is required. 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 where an aircraft landing or taking off via a straight trajectory encounters another aircraft landing or taking off, or transiting by any means. The model shares the Bayesian network foundation of other Massachusetts Institute of Technology 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.
MIT Department
Lincoln Laboratory
Terms of Use
Creative Commons Attribution
https://creativecommons.org/licenses/by/4.0/
Persistent DSpace Link
https://hdl.handle.net/1721.1/140288
DOI of Published Version
https://doi.org/10.3390/aerospace9020058
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