Statistical engineering approach to improve the realism of computer-simulated experiments with aircraft trajectory clustering

This article presents a statistical engineering approach for clustering aircraft trajectories. The clustering methodology was developed to address the need to incorporate more realistic trajectories in fast-time computer simulations used to evaluate an aircraft spacing algorithm. The methodology is...

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Bibliographic Details
Published in:Quality engineering 2017-04, Vol.29 (2), p.167-180
Main Authors: Wilson, Sara R., Swieringa, Kurt A., Leonard, Robert D., Freitag, Evan, Edwards, David J.
Format: Article
Language:English
Subjects:
Aircraft
dynamic time warping
Engineering
gap statistic
k-means
Realism
statistical engineering
trajectory clustering
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Summary:This article presents a statistical engineering approach for clustering aircraft trajectories. The clustering methodology was developed to address the need to incorporate more realistic trajectories in fast-time computer simulations used to evaluate an aircraft spacing algorithm. The methodology is a combination of Dynamic Time Warping and k-Means clustering, and can be viewed as one of many possible solutions to the immediate problem. The implementation of this statistical engineering approach is also repeatable, scalable, and extendable to the investigation of other air traffic management technologies. Development of the clustering methodology is presented in addition to an application and description of results.
ISSN:0898-2112
1532-4222
DOI:10.1080/08982112.2016.1147050