Data-driven methodology for uncertainty quantification of aircraft trajectory predictions
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Cita com:
hdl:2117/366290
Tipus de documentText en actes de congrés
Data publicació2021
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
ProjecteSTART - a Stable and resilient ATM by integrating Robust airline operations into the network (EC-H2020-893204)
PJ07-W2 OAUO - SESAR2020 PJ07-W2 OAUO Optimised Airspace Users Operations (EC-H2020-874465)
PJ07-W2 OAUO - SESAR2020 PJ07-W2 OAUO Optimised Airspace Users Operations (EC-H2020-874465)
Abstract
This work present a framework based on datadriven
techniques for quantifying and chaos theory for propagating
the uncertainty present in the aircraft trajectory prediction
process when computing the expected trajectory from a given
flight plan. The developed framework employs data assimilation
models to capture real-time information from the air traffic
system and introduces a novel methodology in order to account
for the uncertainty of the weather conditions. The comparison of
the resulting set of probabilistic trajectories and the actually
flown ones proves how the former could be a key enabler
to support envisioned trajectory-based operation concepts and
modern airline operations planning.
CitacióMuñoz, A. [et al.]. Data-driven methodology for uncertainty quantification of aircraft trajectory predictions. A: IEEE/AIAA Digital Avionics Systems Conference. "2021 IEEE/AIAA 40th Digital Avionics Systems Conference: San Antonio, TX, USA: October 3-7, 2021". Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 1-10. ISBN 9781665434218. DOI 10.18038/aubtda.270074.
ISBN9781665434218
Versió de l'editorhttps://2021.dasconline.org/
Altres identificadorshttps://elib.dlr.de/145075/
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dasc2021_finalpaper.pdf | 2,209Mb | Visualitza/Obre |