DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Data-driven uncertainty quantification and propagation for probabilistic trajectory planning

Munoz, Andres and Polaina, Manuel and Guemes, Alejandro and Pons, Jordi and Prats, Xavier and Koyuncu, Emre and Delahaye, Daniel and Zopp, Raimund and Kuenz, Alexander and Soler, Manuel (2022) Data-driven uncertainty quantification and propagation for probabilistic trajectory planning. In: 12th EASN International Conference on Innovation in Aviation and Space for opening New Horizons, EASN 2022. 12th EASN International Conference on Innovation in Aviation and Space for opening New Horizons, EASN 2022, 2022-10-18 - 2022-10-21, Barcelona, Spanien.

Full text not available from this repository.


One of the main objectives of Trajectory-Based Operations (TBO) is to increase the predictability of the aircraft behavior within the Air Traffic Management (ATM) system. However, most systems involved in TBO (such as flight planning systems) focus on proposing deterministic trajectories in the strategic phase, not taking into account the uncertainty factors that affect the trajectory prediction process in the tactical phase. Consequently, there is an increased frequency of updates and modifications to trajectories in later planning phases, which leads to degraded stability, resulting in an overall decrease of the performance of the ATM network. In this presentation, a data-driven methodology will be introduced for characterizing the uncertainties affecting the development of an aircraft trajectory, together with their integration into a stochastic trajectory predictor for obtaining robust sets of probabilistic trajectories from an initial flight plan. Additionally, this methodology employs data assimilation models that capture updated information from the air traffic system to reduce the present uncertainty. First, the main sources of uncertainty for aircraft trajectories will be identified and quantified using historical flight instances for a full year of pan-European air traffic. After quantifying these sources of uncertainty, it will be possible to evaluate the potential variations for a flight plan given the probability distributions for uncertain factors affecting the flight. Instead of applying computationally demanding methods, such as Monte Carlo simulations, for calculating all possible trajectories, a stochastic trajectory predictor is proposed that makes use of the characterization of trajectory uncertainty to compute probabilistic trajectories given an initial flight plan. The stochastic trajectory predictor uses arbitrary Polynomial Chaos Expansion (PCE) theory and the point collocation method to find polynomials describing the aircraft trajectory for the initial flight plan as a function of the identified uncertain factors. Therefore, the quantified uncertainty sources can be fitted in the polynomials to find a reduced set of probabilistic trajectories that are robust and resilient to potential variations in the tactical phase. Complementing this, a set of advanced data-assimilation models based on machine learning techniques are integrated to provide accurate estimations for some of the uncertain factors based on the last available status of the air traffic system. These estimates reduce the uncertainty spectrum for important variables in the trajectory prediction process and help adapting the resulting probabilistic trajectories to the current system status. Finally, a study case is introduced in which the proposed methodology is implemented. This study includes the results of analyzing the probabilistic trajectories for one city-pair and supports the idea of integrating probabilistic trajectories as a key enabler for envisioned TBO concepts and modern airline operations planning

Item URL in elib:https://elib.dlr.de/189571/
Document Type:Conference or Workshop Item (Speech)
Title:Data-driven uncertainty quantification and propagation for probabilistic trajectory planning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kuenz, AlexanderUNSPECIFIEDhttps://orcid.org/0000-0001-5192-8894UNSPECIFIED
Date:19 October 2022
Journal or Publication Title:12th EASN International Conference on Innovation in Aviation and Space for opening New Horizons, EASN 2022
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Uncertainty, probabilistic trajectory, uncertainty propagation
Event Title:12th EASN International Conference on Innovation in Aviation and Space for opening New Horizons, EASN 2022
Event Location:Barcelona, Spanien
Event Type:international Conference
Event Start Date:18 October 2022
Event End Date:21 October 2022
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Air Transportation and Impact
DLR - Research area:Aeronautics
DLR - Program:L AI - Air Transportation and Impact
DLR - Research theme (Project):L - Integrated Flight Guidance
Location: Braunschweig
Institutes and Institutions:Institute of Flight Guidance > Pilot Assistance
Deposited By: Kuenz, Dr. Alexander
Deposited On:10 Nov 2022 09:31
Last Modified:24 Apr 2024 20:50

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.