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Aircraft Modeling and Trajectory Prediction using Particle Filters

The process of trajectory prediction is especially of high importance for air traffic controllers as it provides them with an instrument that ensures efficient and conflict free flights. This can be made possible by checking predicted trajectories against each other to identify any conflicts between them. An aircraft following an agreed trajectory can then be assured a trouble free and unperturbed flight. In order for this to be attainable, the trajectory prediction tools should be able to provide a high level of accuracy for an adequate horizon of time.We propose different particle filtering algorithms that can provide this high level of accuracy using sequential Radar measurements for updating the trajectory prediction available to the air traffic controllers. Particle filters are fast estimation techniques based on simulation. They approximate the probability distribution of interest using a large set of random samples named particles. These particles are propagated over time using importance sampling and re-sampling mechanisms. Asymptotically, as the number of particles goes to infinity, the convergence of these particle approximations towards the sequence of probability distributions can be ensured under very weak assumptions. We present the development of a model describing aircraft behavior from the point of view of an air traffic controller, that provides the underlying dynamics propagating such particles. The model is framed in the context of stochastic hybrid systems and consists of many instances of flights, each with different aircraft dynamics, flight plan and flight management system.

Type of Seminar:
IfA Seminar
Ioannis Lymperopoulos
Jun 14, 2007   11am

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