This paper proposes a multi-sensor positioning technology for unmanned aerial vehicle (UAV) landing based on inertial navigation system (INS)/Global navigation satellite system (GNSS)/Radar integrated guidance system. In the harsh environment where sensor prior information is unreliable, measurement noise is non-stationary and measurement outliers are frequently generated, an adaptive federated filter based on variational Bayesian is used to achieve high accuracy and robustness of navigation system. Simulation results demonstrate that this guidance technology has a strong ability to adapt to non-stationary noise and frequent outliers, and the fusion accuracy is satisfactory.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Fu, Wenxing (editor) / Gu, Mancang (editor) / Niu, Yifeng (editor) / Li, Yifan (author) / Hu, Jinwen (author) / Zhao, Chunhui (author) / Xu, Zhao (author) / Lv, Mingwei (author) / Wang, Wenzhe (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2022 ; Xi'an, China September 23, 2022 - September 25, 2022



    Publication date :

    2023-03-10


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Roadside multi-sensor data fusion based on adaptive federal Kalman filtering

    Chai, Congcheng / Yang, Tao / Lyu, Nengchao | IEEE | 2023


    Multi-sensor Data Fusion of UAV Landing System

    Shi, Shasha / Hu, Jinwen / Zhao, Chunhui et al. | TIBKAT | 2022


    Multi-sensor Data Fusion of UAV Landing System

    Shi, Shasha / Hu, Jinwen / Zhao, Chunhui et al. | British Library Conference Proceedings | 2022


    Multi-sensor Data Fusion of UAV Landing System

    Shi, Shasha / Hu, Jinwen / Zhao, Chunhui et al. | Springer Verlag | 2021