This paper proposes a new target motion state estimation method for UAV aerial refueling and autonomous recovery. First, airborne vision measurement, airborne electro‐optical pod, and GPS information are fused to formulate a feasible target estimation model. The problems of unexpected outliers and inconsistent update frequency appearing in these different measurements are alleviated by leveraging multi-model and multi-rate extended Kalman filter algorithm, which can autonomously and optimally select and update the measurement model. Numerous flight experiments demonstrate that the proposed target estimation method has satisfactory accuracy and high robustness in complex interference environment.


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    Title :

    Airborne Vision Based Target Motion State Estimation for UAV Aerial Docking


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Li, Ruoxuan (author) / He, Shaoming (author) / Song, Tao (author) / Tao, Hong (author)

    Conference:

    International Conference on Guidance, Navigation and Control ; 2022 ; Harbin, China August 05, 2022 - August 07, 2022



    Publication date :

    2023-01-31


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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