NAT: Neural network Aircraft Trajectory prediction
La page de présentation de NAT est aussi disponible en
français.
Summary
- Acronym: NAT
- Project name: Neural network Aircraft Trajectory prediction
- Start date: 1996
- End date: 1999
Description
This project deals with the problem of predicting an aircraft
trajectory in the vertical plane. A method depending on a small number
of starting parameters is introduced and then used on a wide range of
cases. The chosen method is based on neural networks. Neural networks
are trained using a set of real trajectories and then used to forecast
new ones. Two prediction methods have been developed: the first is
able to take real points into account as the aircraft flies to improve
precision. The second one predicts trajectories even when the aircraft
is not flying. After depicting those prediction methods, the results
are compared with other forecasting functions. Neural networks give
better results because they only rely on precisely known parameters.
The figure below presents the comparison of the forecasted trajectory
with a real trajectory, on an example that has not been learned by the
network.
Software
Not available.
Useful links
Main publications
Using Neural Networks to predict aircraft trajectories Yann LeFablec, Jean-Marc Alliot
IC-AI 99 Las Vegas
(1999/7/1)
Prévision de trajectoires d'avions par réseaux de neurones Yann Le Fablec
PhD (INPT)
(1999/10/18)
Prévision stochastique de trajectoires : procédures paramétriques et non-paramétriques Christophe Bontemps
Rapport de DEA IFP
(1997/05/25)
Toutes les publications du LOG sont disponibles
ici.