TU Delft / AE / CO / CNS-ATM / Master thesis assignment:
Existing trajectory prediction studies in the literature often focus on getting the prediction right. However, due to existence of many random factors, such as weather, flight procedures, congestion, and regulations, the prediction of flight is not always certain.
In this master thesis project, you will design different methods to tackle the unpredictability of flight trajectories caused by known and unknown random factors. You will make use of stochastic methods to measure and model the uncertainty in flight predictions, for example, by employing the combination of model-based and data-driven approaches. The goal is to model uncertainty caused by these random variable at different phase of flights, as well as to relate them to uncertainty in time and location of the aircraft.
This thesis will make use of several stochastic tools that we have proposed in our early research, such as hierarchical Bayesian computing, Gaussian processing regression, and particle filtering.
In addition to related aeronautics background knowledge, we are looking for an MSc student who:
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