Probabilistic trajectory generation using uncertainty propagation model
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/366688
Tipus de documentReport de recerca
Data publicació2021-06-09
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
This document establishes the basis for the work to be developed within Work Package 2 of the START project. The objective of this Work Package is to build a methodology that could allow for the obtainment of the probabilistic trajectories that would result from the propagation of the characterized micro-level uncertainties in the aircraft trajectory prediction process. This deliverable will be focused on implementing the models and processes required to capture the influence of the uncertainties that are present in the development of an aircraft trajectory. To this end, we will show how to propagate these uncertainties, using a stochastic trajectory predictor, that will allow us to obtain a set of probabilistic trajectories from an initial deterministic flight plan, which will encapsulate the effect of the inputs’ variability.
First, an introduction to Polynomial Chaos Theory, which is the basis of the stochastic trajectory predictor developed in START, and our solution for introducing weather uncertainty into the trajectory prediction process will be exposed. Then, it will be presented how the integration of the advanced data assimilation models, introduced in the deliverable D2.1 [2], together with the stochastic trajectory predictor will lead to more robust airline operations. Additionally, the framework for the probabilistic trajectory generation will be introduced, showing how all different modules will be employed in START in a two-phase approach (first an off-line fitting phase to obtain the models for uncertainty propagation, and then an online phase where, making use of the fitted model, the probabilistic trajectories can be obtained from a deterministic flight plan). Finally, a study case will be presented, showing the application of the previously defined methodology to a specific scenario.
CitacióMuñoz, A. [et al.]. Probabilistic trajectory generation using uncertainty propagation model. 2021.
Fitxers | Descripció | Mida | Format | Visualitza |
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START_D2-2_ed-01-00.pdf | D2.2 | 2,138Mb | Visualitza/Obre |