An algorithm for the flight path reconstruction of state variables and for estimation of unknown constant bias and scale factor errors in measured data using extended Kalman filter and fixed interval smoother was developed. The models are based on the six-degree-of freedom kinematic equations relating measured aircraft responses. The technique is demonstrated with the aid of typical examples using flight test data. Also some sensitivity studies on the variation of noise covariance matrices were performed. The results of FPR using extended Kalman filter and maximum likelihood methods are presented. Finally aerodynamic estimation was carried out with a simple linear model on different data sets names: (1) flight measured data not corrected for bias and scale factor errors; (2) using EKF corrected data; and (3) using ML corrected data.
Flight Path Reconstruction Using Extended Kalman Filtering Techniques
1990
75 pages
Report
No indication
English
AIAA | 2009
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