Print Email Facebook Twitter Aircraft Mass and Thrust Estimation Using Recursive Bayesian Method Title Aircraft Mass and Thrust Estimation Using Recursive Bayesian Method Author Sun, Junzi (TU Delft Control & Simulation) Blom, H.A.P. (TU Delft Air Transport & Operations; Royal Netherlands Aerospace Centre NLR) Ellerbroek, Joost (TU Delft Control & Simulation) Hoekstra, J.M. (TU Delft Control & Simulation) Date 2018 Abstract 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 used for the estimation. An important aspect of particle filtering is noise modeling. Four noise models are proposed in this paper based on the native ADS-B Navigation Accuracy Category (NAC) parameters. Simulations, experiments, and validation, based on a number of flights are carried out to test the theory. As a result, convergence of the estimation can usually be obtained within 30 seconds for any climbing flight. The method proposed in this paper not only provides final estimates, but also defines the limits of noise above which estimation of mass and thrust becomes impossible. When validated with a dataset consisting of the measured true mass and thrust of 50 Cessna Citation II flights, the stochastic recursive Bayesian approach proposed in this paper yields a mean absolute error of 4.6%. Subject aircraftstate estimationpoint-mass modelmeasurement noiseparticle filterBayesian estimation To reference this document use: http://resolver.tudelft.nl/uuid:2b02d90b-9c33-49af-82bb-d32fd4061919 Embargo date 2021-12-08 Source 2018 International Conference on Research in Air Transportation: Barcelona, Spain, 2018 Event ICRAT 2018: 2018 International Conference on Research in Air Transportation, 2018-06-26 → 2018-06-29, Castelldefels, Barcelona, Spain Bibliographical note 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. Part of collection Institutional Repository Document type conference paper Rights © 2018 Junzi Sun, H.A.P. Blom, Joost Ellerbroek, J.M. Hoekstra Files PDF ICRAT_2018_paper_19_1_.pdf 817.93 KB Close viewer /islandora/object/uuid:2b02d90b-9c33-49af-82bb-d32fd4061919/datastream/OBJ/view