Quantification of Aircraft Trajectory Prediction Uncertainty Using Polynomial Chaos Expansions

Casado, E., La Civita, M., Vilaplana, M. and McGookin, E. W. (2017) Quantification of Aircraft Trajectory Prediction Uncertainty Using Polynomial Chaos Expansions. In: 2017 IEEE/AIA 36th Digital Avionics Systems Conference, St. Petersburg, FL, USA, 17-21 Sep 2017, ISBN 9781538603659 (doi: 10.1109/DASC.2017.8102052)

Abstract

A novel approach to quantify the uncertainty associated with any aircraft trajectory prediction based on the application of the Polynomial Chaos (PC) theory is presented. The proposed method relies on univariate polynomial descriptions of the uncertainty sources affecting the trajectory prediction process. Those descriptions are used to build the multivariate polynomial expansions that represent the variability of the aircraft state variables along the predicted trajectory. A case study compares the results obtained by a classical Monte Carlo approach with those generated by applying the so-called arbitrary Polynomial Chaos Expansions (aPCE). The results provided herein lead to conclude that this new methodology can be used to accurately quantify trajectory prediction uncertainty with a very low computational effort, enabling the capability of computing the uncertainty of the individual trajectories of a traffic sample of thousands flights within very short time intervals.

• Item Type: Conference Proceedings
• Additional Information: This project has received funding from the SESAR Joint Undertaking under grant agreement No 699274 under European Union’s Horizon 2020 research and innovation programme.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: McGookin, Dr Euan
• Authors: Casado, E., La Civita, M., Vilaplana, M., and McGookin, E. W.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• ISBN: 9781538603659
• Published Online: 09 November 2017
• Copyright Holders: Copyright © 2017 IEEE
• First Published: First published in 2017 IEEE/AIA 36th Digital Avionics Systems Conference
• Publisher Policy: Reproduced in accordance with the publisher copyright policy