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Inference of a random environment from random process realizations : formalism and application to trajectory prediction

Cécile Ichard 1, * Christophe Baehr 2
* Corresponding author
1 MAIAA-PROBA - Equipe MAIAA-PROBA
MAIAA - ENAC - Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l'Aérien
Abstract : We are interested in aircraft trajectories seen as stochastic processes. These processes evolve in an unknown atmospheric random environnment. As several aircraft parameters are unknown, such as true airspeed (TAS) and wind, we have to estimate them. To this end, we suggest to use ensemble weather forecasts, which give different scenarios for the atmosphere, with a system of trajectory predictions. In this way, we evaluate the likelihood of each element and we construct a random weather environment organized by the element weight. To get this result, we use sequential Monte Carlo methods (SMC) in the special context of random environment. We propose to use particle Markov chain Monte Carlo method (pMCMC) to estimate the aircraft parameters.
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Cécile Ichard, Christophe Baehr. Inference of a random environment from random process realizations : formalism and application to trajectory prediction. ISIATM 2013, 2nd International Conference on Interdisciplinary Science for Innovative Air Traffic Management, Jul 2013, Toulouse, France. ⟨hal-00867898⟩

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