Bayesian Inference of Aircraft Initial Mass

Aircraft mass is a crucial piece of information for studies on aircraft performance, trajectory prediction, and many other ATM topics. However, it is a common challenge for researchers who have no access to this proprietary information. Previously, several studies have proposed methods to estimates aircraft weight, most of which are focused on specific parts of the flight. Often due to inaccurate input data or biased assumptions, a significant number of estimates can result outside of the weight limitation boundaries. This paper proposes an approach that makes use of multiple observations to get a better estimate for a complete flight. By looking at flight data from a complete trajectory and calculating aircraft mass at different flight phases based on different methods, together with fuel flow models, multiple observations of aircraft initial mass can then be derived. Using the Bayesian inference method, final estimates can be made with a higher level of confidence.