Aircraft Atypical Approach Detection using Functional Principal Component Analysis

Abstract : Airports Terminal Maneuvering Areas (TMA) and Control Traffic Regions (CTR) are characterized by a dense air traffic flow with high complexity. In nominal operations, approach flight path safety management consists in procedures which guide the aircraft to intercept the final approach axis, and the runway slope with an expected configuration in order to land. Some abnormal flights are observed and considered as Non Compliant when the intermediate and the final leg intercepting conditions do not comply with the prescription of the operational documentation. This kind of trajectories generates difficulties for both crew and Air Traffic Control (ATC) and may induce undesirable events such as Non Stabilized Approaches or ultimate events like Control Flight Into Terrain (CFIT), in the worst cases. There is a real need for atypical flights detection tools in order to improve safety. In this paper, a post-operational detection method based on functional principal component analysis and unsupervised learning will be presented and compared to geometric features.
Type de document :
Communication dans un congrès
SESAR Innovations Days 2018, Dec 2018, Salzburg, Austria. 2018, Proceedings SID 2018, SESAR Innovations Days
Liste complète des métadonnées

https://hal-enac.archives-ouvertes.fr/hal-01944595
Contributeur : Laurence Porte <>
Soumis le : mardi 4 décembre 2018 - 17:30:48
Dernière modification le : vendredi 14 décembre 2018 - 01:07:48

Fichier

SIDs_2018_paper_30.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01944595, version 1

Collections

Citation

Gabriel Jarry, Daniel Delahaye, Florence Nicol, Eric Féron. Aircraft Atypical Approach Detection using Functional Principal Component Analysis. SESAR Innovations Days 2018, Dec 2018, Salzburg, Austria. 2018, Proceedings SID 2018, SESAR Innovations Days. 〈hal-01944595〉

Partager

Métriques

Consultations de la notice

27

Téléchargements de fichiers

33