Modelling the dispersion of aircraft trajectories using Gaussian processes


Eerland, Willem, Box, Simon and Sobester, Andras (2016) Modelling the dispersion of aircraft trajectories using Gaussian processes Journal of Guidance, Control, and Dynamics, pp. 1-28. (doi:10.2514/1.G000537).

Download

[img] PDF paper_jgcd_preprint.pdf - Accepted Manuscript
Available under License University of Southampton Accepted Manuscript Licence.

Download (4MB)
[img] PDF paper.pdf - Other
Available under License University of Southampton Accepted Manuscript Licence.

Download (4MB)

Description/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%.

Item Type: Article
Digital Object Identifier (DOI): doi:10.2514/1.G000537
ISSNs: 0731-5090 (print)
Related URLs:
Organisations: Computational Engineering & Design Group, Transportation Group
ePrint ID: 399818
Date :
Date Event
21 March 2016Submitted
17 June 2016Accepted/In Press
25 August 2016e-pub ahead of print
Date Deposited: 30 Aug 2016 09:23
Last Modified: 16 Jun 2017 16:37
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/399818

Actions (login required)

View Item View Item