Accession Number : AD1051496


Title :   Correlated Encounter Model for Cooperative Aircraft in the National Airspace System; Version 2.0


Descriptive Note : Technical Report


Corporate Author : Massachusetts Institute Technology Lincoln Laboratory Lexington United States


Personal Author(s) : Underhill, N ; Harkleroad,E ; Guendel,R ; Maki,D ; Edwards,M


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1051496.pdf


Report Date : 08 May 2018


Pagination or Media Count : 140


Abstract : Detect and avoid (DAA) systems are designed to enable safe operation of unmanned aircraft systems (UAS) in the U.S. National Airspace System (NAS). Testing DAA systems before fielding is necessary to validate system robustness and confirm the system's safety benefit. The Extended Correlated Encounter Model (ECEM) generates encounters with trajectories beginning 110 seconds prior to Closest Point of Approach (CPA), enabling testing of the largest DAA alerting conditions that have been defined by the Radio Technical Commission for Aeronautics (RTCA). This model generates statistically representative encounters between two aircraft in the U.S. National Airspace System (NAS) in which both aircraft are cooperative (carrying a transponder) and at least one is receiving Air Traffic Control (ATC) services. Similar to the 2008 Correlated Encounter Model for Cooperative Aircraft (CEM), the ECEM uses a Bayesian network to capture encounter feature correlations and uses a Markov model to generate sensible and smooth aircraft maneuvers. The ECEM allows horizontal and vertical maneuvers and, because of the extended duration, it additionally allows acceleration variations. The ECEM also explicitly models airspeeds, vertical rates, turn rates, altitude layer, and airspace class enabling specific encounter subsets to be generated by limiting or specifying variable values in the encounter model structure.


Descriptors :   airborne collision avoidance systems , airport radar systems , bayesian networks , air traffic control systems , accuracy , algorithms , probability , unmanned aerial vehicles , correlation


Subject Categories : Air Navigation and Guidance


Distribution Statement : APPROVED FOR PUBLIC RELEASE