Regression modelling of trajectory measurement data was examined as a means for improving the performance of aircraft trajectory tracking and prediction. Regression models were used for adaptively removing measurement noise from trajectory observations and extrapolating trajectory measurements. A comparative study was done between three models of aircraft dynamics used in an extended Kalman filter: a strictly translational model, and an attitude/translation model that uses vehicle specific inertial characteristics. Adaptive regression models were used for measurement accuracy enhancement. Comparisons were also made between errors resulting from position and a predictions using Runge-Kutta integration and extrapolated regression models. Tracking, Trajectory, Kalman filter, Estimation, Regression, Trackers.
Aircraft Trajectory Tracking and Prediction
1993
56 pages
Report
Keine Angabe
Englisch
Antiaircraft Defense Systems , Predictions , Tracking , Trajectories , Accuracy , Aircraft , Augmentation , Comparison , Dynamics , Errors , Filters , Integration , Measurement , Models , Noise , Observation , Vehicles , Kalman filtering , Estimates , Antiaircraft defense , Translations
Aircraft Trajectory Tracking and Prediction
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