Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

Data-driven Conflict Detection Enhancement in Closest Point of Approach Problem

Abstract : Closest Point of Approach (CPA) is one of the main problems in aircraft Conflict Detection (CD). It aims to find out the minimum distance and the associated time between two aircraft. Conventional CPA calculation algorithm generally assumes that the speed and heading of aircraft are constant. But the uncertainties in real operational scenarios lead to inaccuracy of CPA calculation. This project presents a novel data-driven CD framework with Machine Learning (ML) algorithms. The proposed framework provides a promising solution for improving the CPA prediction accuracy with the help of real trajectory data. It contributes to not only reduce the number of fault Short-mid term conflict alert for air traffic controllers, but also support the implementation of future free flight concept, so as to reduce fuel consumption and emission.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

https://hal-enac.archives-ouvertes.fr/hal-02388307
Contributeur : Laurence Porte Connectez-vous pour contacter le contributeur
Soumis le : dimanche 15 décembre 2019 - 23:23:29
Dernière modification le : mardi 19 octobre 2021 - 11:02:54
Archivage à long terme le : : lundi 16 mars 2020 - 14:12:00

Fichier

Roll up SIDs 2019-06 red.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-02388307, version 2

Collections

Citation

Zhengyi Wang, Man Liang, Daniel Delahaye. Data-driven Conflict Detection Enhancement in Closest Point of Approach Problem. SID 2019, 9th SESAR Innovation Days, Dec 2019, Athenes, Greece. ⟨hal-02388307v2⟩

Partager

Métriques

Consultations de la notice

164

Téléchargements de fichiers

40