Adaptive Air-Data Estimation in Wind Disturbance Based on Flight Data

Abstract Air-data, recorded in the flight data of civil aviation aircraft, can be disturbed by wind disturbance, thus leading to measurement error. A two-stage air-data estimation in wind disturbance was studied to obtain accurate true airspeed, angle of attack, and sideslip angle based on flight data. To separate the prevailing wind from the wind disturbance, the first stage involved the preliminary air-data optimization by the immune clone algorithm (ICA) and prevailing wind optimization by the Gauss-Newton algorithm. In the second stage, a new filtering system combining air-data and the von Kármán turbulence model was built with the initial value provided by the first stage. A weighted adaptive extended Kalman filtering (WAEKF) algorithm was proposed, in which an exponential weighting in the innovation covariance matrix was used to reduce the estimation error further. Simulation results indicate that the optimized initial value provided by the first stage is fundamental to ensuring the convergence rate and stability. The WAEKF algorithm with initial value (WAEKF-INIT) can improve the estimation accuracy and alleviate the effects of uncertain measuring noise. A further test with flight data shows that weighted adaptive filtering is capable of reducing the estimation error further in uncertain disturbance.


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  • Accession Number: 01833863
  • Record Type: Publication
  • Files: TRIS, ASCE
  • Created Date: Jan 24 2022 5:27PM