Aiming to reduce the uncertainty of the trajectory of Continuous Descent Operations (CDO) in high-density airspace, this paper combines the unscented transformation and Kalman filtering, and a dynamic flight trajectory prediction algorithm based on Unscented Kalman Filter (UKF) is proposed. Firstly, according to the real-time ADS-B data, the position and speed of the aircraft are converted. Then, to improve the prediction accuracy of the dynamic flight trajectory, the aircraft state equations are processed by unscented transformation, and a dynamic trajectory prediction model based on UKF is established. Finally, taking the CES2492 flight as an example, a fast-time simulation is carried out, and the results are compared with those obtained by the traditional Kalman filter and the extended Kalman filter. The simulation results show that the method proposed in this paper can improve the filtering effect and effectively enhance the trajectory prediction accuracy.


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    Titel :

    Dynamic Trajectory Prediction for Continuous Descend Operations Based on Unscented Kalman Filter


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jia, Yingmin (Herausgeber:in) / Zhang, Weicun (Herausgeber:in) / Fu, Yongling (Herausgeber:in) / Zhang, Jun (Autor:in) / Wang, Guoqing (Autor:in) / Xiao, Gang (Autor:in)

    Kongress:

    Chinese Intelligent Systems Conference ; 2020 ; Shenzhen City, China October 24, 2020 - October 25, 2020



    Erscheinungsdatum :

    24.09.2020


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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