M. Strauss

This work aims to improve the flight dynamic model of the model aircraft Vitesse V2 through parameter identification using an extended and unscented Kalman filter (EKF and UKF). A flight mechanical model is integrated into the filter algorithms in MATLAB to estimate the aircraft’s states and aerodynamic coefficients. Two variants of the UKF are examined: a general case with nonlinear noise and a simplified case assuming additive zero-mean noise. By conducting flight tests, data is obtained to optimize the filter design parameters as well as for the actual parameter identification. The results show that the simplified UKF provides the best balance between capturing system dynamics and ensuring parameter convergence. Its superior performance over the EKF is likely due to the sigma point method, which enhances the accuracy of state and parameter estimates. Focusing on aerodynamic parameter estimation and using longer datasets significantly improves accuracy and convergence of the estimated parameters.

Deutscher Luft- und Raumfahrtkongress 2024, Hamburg

Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn, 2025

Conference Paper

englisch

21,0 x 29,7 cm, 12 Seiten

urn:nbn:de:101:1-2503051259476.975366633927

10.25967/630541

Aerodynamic Parameter Identification, Extended Kalman Filter, Unscented Kalman Filter

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Strauss, M. (2025): Parameter Identification via Kalman Filter on a Model Motor Glider. Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.. (Text). https://doi.org/10.25967/630541. urn:nbn:de:101:1-2503051259476.975366633927.

05.03.2025