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Semantic Scholar · Article (Symposium on Dependable Autonomic and Secure Computing)

Aircraft Mass Estimation using Quick Access Recorder Data

Published 2018-09-01 From Symposium on Dependable Autonomic and Secure Computing 4 authors

Attribution

This is the abstract and citation. Full text lives at Semantic Scholar — we link out rather than host. All credit to the authors and Symposium on Dependable Autonomic and Secure Computing.

Abstract

Verbatim from Semantic Scholar. Not paraphrased, not summarized.

Aircraft mass is the most important parameter airliners use to determine how much cargo and fuel they should take for the flight to maximize their profit while keep the total weight under the safety limit. For many practical reasons, airliners can not get the accurate aircraft mass data by weighting every cargo and passenger for each flight. Several studies have proposed methods to estimate aircraft mass based on radar data or ADS-B data. But due to the measurement errors in the data, and also due to uncertainties in aircraft configuration, it is hard to calculate accurate aircraft mass data for every flight. In the paper, the quick access recorder data are used for analysis, parameters that are not available in radar data or ADS-B data can now be used to eliminate parameters with large errors in the level flight point-mass dynamics model. Equations are reformulated as a set of overdetermined linear equations with nonlinearly structured errors in system matrix. The set of equations does not depend on inaccurate parameters like thrust and it does not require accurate knowledge of the aircraft like geometry, aerodynamic coefficients, etc. The set of equations is solved by an improved structured nonlinear total least squares method using Monte Carlo method. The method is applied to 120 real flights of Boeing 777-300ER aircraft, the result shows a good accuracy for some type of flights.

Authors

  • Fang He
  • Lishuai Li
  • Weizun Zhao
  • Gang Xiao

Keywords

  • Computer Science
  • Engineering

Citation: Fang He, Lishuai Li, Weizun Zhao , et al. (2018). Aircraft Mass Estimation using Quick Access Recorder Data. Symposium on Dependable Autonomic and Secure Computing. Semantic Scholar ID 4cd93c6ed3dfcd40daf88e47d7f5d93c2613b4f3. https://doi.org/10.1109/DASC.2018.8569866 ↗