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Modelling the dispersion of aircraft trajectories using Gaussian processes

Modelling the dispersion of aircraft trajectories using Gaussian processes
Modelling the dispersion of aircraft trajectories using Gaussian processes
This work investigates the application of Gaussian processes to capturing the probability distribution of a set of aircraft trajectories from historical measurement data. To achieve this, all data are assumed to be generated from a probabilistic model that takes the shape of a Gaussian process.

The approach to Gaussian process modelling used here is based on a linear expansion of trajectory data into set of basis functions that may be parametrized by a multivariate Gaussian distribution. The parameters are learned through maximum likelihood estimation.

The resulting probabilistic model can be used for both modelling the dispersion of trajectories along the common flightpath and for generating new samples that are similar to the historical data.

The performance of this approach is evaluated using three trajectory datasets; toy trajectories generated from a Gaussian distribution, sounding rocket trajectories that are generated by a stochastic rocket flight simulator and aircraft trajectories on a given departure path from DFW airport, as measured by ground-based radar. The results indicate that the maximum deviation between the probabilistic model and test data obtained for the three data sets are respectively 4.9%, 7.6% and 13.1%.
0731-5090
2661-2672
Eerland, Willem
7f5826c3-536f-4fdc-955e-0f9870c96a0e
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Eerland, Willem
7f5826c3-536f-4fdc-955e-0f9870c96a0e
Box, Simon
2bc3f3c9-514a-41b8-bd55-a8b34fd11113
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b

Eerland, Willem, Box, Simon and Sobester, Andras (2016) Modelling the dispersion of aircraft trajectories using Gaussian processes. Journal of Guidance Control and Dynamics, 39 (12), 2661-2672. (doi:10.2514/1.G000537).

Record type: Article

Abstract

This work investigates the application of Gaussian processes to capturing the probability distribution of a set of aircraft trajectories from historical measurement data. To achieve this, all data are assumed to be generated from a probabilistic model that takes the shape of a Gaussian process.

The approach to Gaussian process modelling used here is based on a linear expansion of trajectory data into set of basis functions that may be parametrized by a multivariate Gaussian distribution. The parameters are learned through maximum likelihood estimation.

The resulting probabilistic model can be used for both modelling the dispersion of trajectories along the common flightpath and for generating new samples that are similar to the historical data.

The performance of this approach is evaluated using three trajectory datasets; toy trajectories generated from a Gaussian distribution, sounding rocket trajectories that are generated by a stochastic rocket flight simulator and aircraft trajectories on a given departure path from DFW airport, as measured by ground-based radar. The results indicate that the maximum deviation between the probabilistic model and test data obtained for the three data sets are respectively 4.9%, 7.6% and 13.1%.

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More information

Submitted date: 21 March 2016
Accepted/In Press date: 17 June 2016
e-pub ahead of print date: 25 August 2016
Published date: December 2016
Organisations: Computational Engineering & Design Group, Transportation Group

Identifiers

Local EPrints ID: 399818
URI: http://eprints.soton.ac.uk/id/eprint/399818
ISSN: 0731-5090
PURE UUID: ac60fd77-b295-4461-abf9-42595acfa862
ORCID for Willem Eerland: ORCID iD orcid.org/0000-0002-4559-6122
ORCID for Andras Sobester: ORCID iD orcid.org/0000-0002-8997-4375

Catalogue record

Date deposited: 30 Aug 2016 09:23
Last modified: 15 Mar 2024 03:13

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Contributors

Author: Willem Eerland ORCID iD
Author: Simon Box
Author: Andras Sobester ORCID iD

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