As an important type of dynamic data-driven application system, unmanned aerial vehicles (UAVs) are widely used for civilian, commercial, and military applications across the globe. An increasing research effort has been devoted to trajectory prediction for non-cooperative UAVs to facilitate their collision avoidance and trajectory planning. Existing methods for UAV trajectory prediction typically suffer from two major drawbacks: inadequate uncertainty quantification of the impact of external factors (e.g., wind) and inability to perform online detection of abrupt flying pattern changes. This paper proposes a Gaussian process regression (GPR)-based trajectory prediction framework for UAVs featuring three novel components: 1) GPR with uniform confidence bounds for simultaneous predictive uncertainty quantification, 2) online trajectory change-point detection, and 3) adaptive training data pruning. The paper also demonstrates the superiority of the proposed framework to competing trajectory prediction methods via numerical studies using both simulation and real-world datasets.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Efficient and Robust Online Trajectory Prediction for Non-Cooperative Unmanned Aerial Vehicles


    Beteiligte:
    Xie, Guangrui (Autor:in) / Chen, Xi (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.02.2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Robust trajectory planning for unmanned aerial vehicles in uncertain environments

    Luders, Brandon (Brandon Douglas) | DSpace@MIT | 2008

    Freier Zugriff

    Online environmentally adaptive trajectory planning for rotorcraft unmanned aerial vehicles

    Tong, Chunming / Liu, Zhenbao / Dang, Qingqing et al. | Emerald Group Publishing | 2022


    Optimizing trajectory of unmanned aerial vehicles

    MARRIOTT JACK / TEZEL BIRCE / LIU ZHANG et al. | Europäisches Patentamt | 2021

    Freier Zugriff