Bayesian Inference of Aircraft Operating Speeds for Stochastic Medium-Term Trajectory Prediction

The introduction of trajectory-based operations enables user-preferred routing for aircraft, but it also increases the complexity of the traffic for air traffic control. Thus, there is a need for advanced trajectory prediction to maintain a safe and orderly flow. The execution of a planned trajectory is burdened with uncertainties, e.g. due to weather forecast errors or gaps in the flight intent. In this paper, we propose a method to quantify these uncertainty sources and infer unknown state variables from surveillance data. The proposed model uses Bayesian inference to estimate operating speeds as true airspeed, Mach number, or Cost Index for trajectory prediction. The uncertainties in wind speed, direction, and temperature are quantified from the global ensemble forecasting system. The selected approach allows for the introduction of various types of uncertainties and the output of probability densities of the operating speeds for stochastic trajectory prediction