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Teetool -- a probabilistic trajectory analysis tool Cover

Teetool -- a probabilistic trajectory analysis tool

Open Access
|May 2017

Figures & Tables

Figure 1

An example of a route model, capturing the main axis and borders of a specific route [2].

Figure 2

A sequence diagram visualising the chain of events following the initialization of a World object and the modelling via resampling.

Figure 3

A schematic representation of the seven classes found within Teetool, and the interaction with the user.

Figure 4

Gaussian distributed artificially generated trajectory data (a), aimed at demonstrating the functionality of the confidence region (b).

Figure 5

Rocket flight trajectory data (lines) as produced by a stochastic rocket simulator in uncertain launch- and atmospheric conditions, including the corresponding one standard deviation confidence region (shaded volume).

Figure 6

Aircraft trajectory data of approaching aircraft as measured by a ground-based radar at the Dallas/Fort Worth (DFW) airport, bundled in five common flightpaths.

Figure 7

Confidence regions (i.e. flight corridors) at a constant altitude (i.e. a horizontal plane intersection of the flight corridors), and mean trajectories (dashed lines), as generated from the trajectory data seen in Figure 6.

DOI: https://doi.org/10.5334/jors.163 | Journal eISSN: 2049-9647
Language: English
Submitted on: Jan 31, 2017
Accepted on: May 4, 2017
Published on: May 17, 2017
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2017 Willem Eerland, Simon Box, Hans Fangohr, András Sóbester, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.