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Communication dans un congrès

Statistical prediction of aircraft trajectory : regression methods vs point-mass model

Mohammad Ghasemi Hamed 1, * David Gianazza 2, 3 Mathieu Serrurier 4 Nicolas Durand 2, 3 
* Auteur correspondant
1 MAIA-OPTIM - ENAC Equipe MAIAA-OPTIM
MAIAA - ENAC - Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l'Aérien
3 IRIT-APO - Algorithmes Parallèles et Optimisation
IRIT - Institut de recherche en informatique de Toulouse
4 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
IRIT - Institut de recherche en informatique de Toulouse
Abstract : Ground-based aircraft trajectory prediction is a critical issue for air traffic management. A safe and efficient prediction is a prerequisite for the implementation of automated tools that detect and solve conflicts between trajectories. Moreover, regarding the safety constraints, it could be more reasonable to predict intervals rather than precise aircraft positions . In this paper, a standard point-mass model and statistical regression method is used to predict the altitude of climbing aircraft. In addition to the standard linear regression model, two common non-linear regression methods, neural networks and Loess are used. A dataset is extracted from two months of radar and meteorological recordings, and several potential explanatory variables are computed for every sampled climb segment. A Principal Component Analysis allows us to reduce the dimensionality of the problems, using only a subset of principal components as input to the regression methods. The prediction models are scored by performing a 10-fold cross-validation. Statistical regression results method appears promising. The experiment part shows that the proposed regression models are much more efficient than the standard point-mass model. The prediction intervals obtained by our methods have the advantage of being more reliable and narrower than those found by point-mass model.
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Communication dans un congrès
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https://hal-enac.archives-ouvertes.fr/hal-00911709
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Soumis le : vendredi 29 novembre 2013 - 16:31:44
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  • HAL Id : hal-00911709, version 1

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Mohammad Ghasemi Hamed, David Gianazza, Mathieu Serrurier, Nicolas Durand. Statistical prediction of aircraft trajectory : regression methods vs point-mass model. ATM 2013, 10th USA/Europe Air Traffic Management Research and Development Seminar, Jun 2013, Chicago, United States. pp xxxx. ⟨hal-00911709⟩

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