Aircraft Mass and Thrust Estimation Using Recursive Bayesian Method

This paper focuses on estimating aircraft mass and thrust setting using a recursive Bayesian method called particle filtering. The method is based on a nonlinear state-space system derived from aircraft point-mass performance models. Using solely ADS-B and Mode-S data, flight states such as position, velocity, and wind speed are collected and...

Ground-based Wind Field Construction from Mode-S and ADS-B Data with a Novel Gas Particle Model

Wind is an important parameter in many air traffic management researches, as it often introduces significant uncertainties in aircraft performance studies and trajectory predictions. Obtaining accurate wind field information has always been a challenge due to the availability of weather sensors. Traditionally, there is no direct method to...

Modeling and Inferring Aircraft Takeoff Mass from Runway ADS-B Data

Aircraft mass is an important parameter in many ways, either to build aircraft performance models, to predict flight trajectories, or to simulate air traffic. Mass data is usually considered as sensitive information for airlines and is, therefore, not disclosed to researchers publicly. In this paper, we use two methods to infer the mass of an...