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Encounter-Based Simulation Architecture for Detect-And-Avoid ModelingThis paper presents an encounter-based simulation architecture developed at NASA to facilitate flexible and efficient Detect and Avoid modeling in parametric or tradespace studies on large data sets. The basic premise of this tool is that large-scale input data can be reduced to a set of `canonical encounters' and that using the reduced data in simulations does not lead to loss of fidelity. A canonical encounter is specified as ownship and intruder flight portions potentially resulting in a loss of well clear along with a set of properties that characterize the encounter. The advantages of using canonical encounters include faster simulations, reduced memory footprint, ability to select encounters based on user-specified criteria, shared encounters across multiple teams, peer-reviewed encounters, and a better understanding of the input data set, to name a few.
Document ID
20190000084
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Refai, Mohamad
(Crown Consulting, Inc. Washington, DC, United States)
Abramson, Michael
(Crown Consulting, Inc. Washington, DC, United States)
Lee, Seungman
(Crown Consulting, Inc. Washington, DC, United States)
Wu, Gilbert
(NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
January 16, 2019
Publication Date
January 9, 2019
Subject Category
Air Transportation And Safety
Report/Patent Number
ARC-E-DAA-TN62918
Meeting Information
Meeting: AIAA SciTech Forum 2019
Location: San Diego, CA
Country: United States
Start Date: January 7, 2019
End Date: January 11, 2019
Sponsors: American Inst. of Aeronautics and Astronautics
Funding Number(s)
CONTRACT_GRANT: NNA16BD14C
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
MOPS
Simulation
Software
Fast-time
Architecture
DAA
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