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.


    Access

    Access via TIB

    Check availability in my library


    Export, share and cite



    Title :

    Aircraft Trajectory Tracking and Prediction


    Contributors:
    L. C. Cattani (author) / P. J. Eagle (author) / Z. Lin (author) / X. Liu (author) / F. P. Kuhl (author)

    Publication date :

    1993


    Size :

    56 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




    Aircraft Trajectory Tracking and Prediction

    L. C. Cattani / P. J. Eagle / Z. Lin et al. | NTIS | 1992


    Aircraft Identification, Trajectory Tracking and Prediction

    L. C. Cattani / P. J. Eagle | NTIS | 1995


    Trajectory Prediction for Light Aircraft

    Maeder, Urban / Morari, Manfred / Baumgartner, Thomas Ivar | AIAA | 2011


    Long Range Aircraft Trajectory Prediction

    Tone Magister | DOAJ | 2009

    Free access

    Trajectory Prediction for Light Aircraft

    Maeder, U | Online Contents | 2011