Wind Networking Applied to Aircraft Trajectory Prediction

Abstract : Trajectory prediction estimates the future position of aircraft along their planned trajectories in order to detect potential conflicts and to optimize air space occupancy. This prediction is a critical task in the Air Traffic Control (ATC) process and has been studied for many years. For the future automation processes developed in the SESAR [1], NextGen [2] and CARATS [3] projects, such trajectory prediction will be even more critical. In these projects the trajectory predictors generate aircraft forecast trajectories, typically for client applications. As there is always a deviation between the predicted wind (from the weather forecasts) and the encountered wind, the main longitudinal (along-track) error source between the predicted and the actual trajectory is linked to wind estimation. Based on the current performances of Air Traffic Control systems, controllers are able to efficiently detect conflict 20 minutes in advance; for a larger time horizon (look-ahead time), the induced trajectory prediction uncertainty strongly reduces the reliability of the conflict detection. The goal of this work is to measure the potential benefit produced by sharing wind measures between aircraft (this concept will be called wind networking (WN)). To reach this goal, aircraft measure their local atmospheric data (wind, temperature, density and pressure) and broadcast them to the other aircraft. Having such distributed weather information, each aircraft is able to compute an enhanced local wind map as a function of location (3D) and time. These updated wind fields could be shared with other aircraft and/or with ground systems. Using this enhanced weather information, each aircraft is able to improve drastically its own trajectory prediction. This concept has been simulated in the French airspace with 8 000 flights. Comparisons have been investigated on trajectory prediction performances with and without wind networking. Statistics have been conducted in order to measure the bene- it of such concept in both time and space dimensions showing higher improvement in high traffic areas, as expected.
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DASC 2015 IEEE/AIAA 34th Digital Avionics Systems Conference, Sep 2015, Prague, Czech Republic. IEEE, pp.1A4-1-1A410 / 978-1-4799-8939-3, 2015, Digital Avionics Systems Conference (DASC), 2015 IEEE/AIAA 34th. 〈10.1109/DASC.2015.7311330〉
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Karim Legrand, Christophe Rabut, Daniel Delahaye. Wind Networking Applied to Aircraft Trajectory Prediction. DASC 2015 IEEE/AIAA 34th Digital Avionics Systems Conference, Sep 2015, Prague, Czech Republic. IEEE, pp.1A4-1-1A410 / 978-1-4799-8939-3, 2015, Digital Avionics Systems Conference (DASC), 2015 IEEE/AIAA 34th. 〈10.1109/DASC.2015.7311330〉. 〈hal-01224188〉

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