Aircraft initial mass estimation using Bayesian inference method

Aircraft initial mass estimation using Bayesian inference method

Sun, Junzi (TU Delft Control & Simulation)
Ellerbroek, Joost (TU Delft Control & Simulation)
Hoekstra, J.M. (TU Delft Control & Simulation)

2018

Aircraft mass is a crucial piece of information for studies on aircraft performance, trajectory prediction, and many other topics of aircraft traffic management. However, It is a common challenge for researchers, as well as air traffic control, to access this proprietary information. Previously, several studies have proposed methods to estimate aircraft weight based on specific parts of the flight. Due to inaccurate input data or biased assumptions, this often leads to less confident or inaccurate estimations. In this paper, combined with a fuel-flow model, different aircraft initial masses are computed independently using the total energy model and reference model at first. It then adopts a Bayesian approach that uses a prior probability of aircraft mass based on empirical knowledge and computed aircraft initial masses to produce the maximum a posteriori estimation. Variation in results caused by dependent factors such as prior, thrust and wind are also studied. The method is validated using 50 test flights of a Cessna Citation II aircraft, for which measurements of the true mass were available. The validation results show a mean absolute error of 4.3% of the actual aircraft mass.

Aircraft mass
Weight estimation
Bayesian inference

http://resolver.tudelft.nl/uuid:611693b2-046a-4cbc-a61b-d1793e52f523

https://doi.org/10.1016/j.trc.2018.02.022

2018-09-10

0968-090X

Transportation Research. Part C: Emerging Technologies, 90, 59-73

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2018 Junzi Sun, Joost Ellerbroek, J.M. Hoekstra