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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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| Published in: | Quality engineering 2017-04, Vol.29 (2), p.167-180 |
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| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c286t-7b78d657d0f5d236eb547589ce00c9dadf9d93ebd8fa15ce7a7cfb6fdea8e2063 |
| container_end_page | 180 |
| container_issue | 2 |
| container_start_page | 167 |
| container_title | Quality engineering |
| container_volume | 29 |
| creator | Wilson, Sara R. Swieringa, Kurt A. Leonard, Robert D. Freitag, Evan Edwards, David J. |
| description | 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. |
| doi_str_mv | 10.1080/08982112.2016.1147050 |
| format | article |
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| fulltext | fulltext |
| identifier | ISSN: 0898-2112 |
| ispartof | Quality engineering, 2017-04, Vol.29 (2), p.167-180 |
| issn | 0898-2112 1532-4222 |
| language | eng |
| recordid | cdi_proquest_journals_1873380771 |
| source | Business Source Ultimate; Taylor and Francis:Jisc Collections:Taylor and Francis Read and Publish Agreement 2024-2025:Science and Technology Collection (Reading list) |
| subjects | Aircraft dynamic time warping Engineering gap statistic k-means Realism statistical engineering trajectory clustering |
| title | Statistical engineering approach to improve the realism of computer-simulated experiments with aircraft trajectory clustering |
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