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Trajectory Prediction by Adaptively Reducing Wind Uncertainty

Author(s):

I. Lymperopoulos, J. Lygeros
Conference/Journal:

Application of Advanced Technologies in Transportation, Athens
Abstract:

We formulate the problem of aircraft trajectory prediction as a Bayesian estimation problem. We model the aircraft dynamics as a stochastic nonlinear hybrid system. We acquire partial information about the state of the aircraft sequentially through radar measurements. Such observations incorporate information about the missing parameters of the system and the effect of the stochastic part of the dynamics on the system state. The nonlinear, non-Gaussian nature of the model prevents the use of traditional filtering methods such as Kalman filtering, which may otherwise provide optimal solutions for linear systems with additive Gaussian noise. To confront this problem we employ sequential Monte Carlo methods (particle filters) in order to provide a numerical solution to the recursive Bayesian estimation problem. We further improve on the trajectory prediction accuracy by incorporating radar measurements from multiple aircraft flying simultaneously in the same part of the airspace.

Year:

2008
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@InProceedings { LymLyg:2008:IFA_3156,
    author={I. Lymperopoulos and J. Lygeros},
    title={{Trajectory Prediction by Adaptively Reducing Wind
	  Uncertainty}},
    booktitle={Application of Advanced Technologies in Transportation},
    pages={},
    year={2008},
    address={Athens},
    month=may,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3156}
}
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