In addition to outdoor environments, unmanned aerial vehicles (UAVs) also have a wide range of applications in indoor environments. The complex and changeable indoor environment and relatively small space make indoor localization of UAVs more difficult and urgent. An innovative 3D localization method for indoor UAVs using a Wasserstein generative adversarial network (WGAN) and a pseudo fingerprint map (PFM) is proposed in this paper. The primary aim is to enhance the localization accuracy and robustness in complex indoor environments. The proposed method integrates four classic matching localization algorithms with WGAN and PFM, demonstrating significant improvements in localization precision. Simulation results show that both the WGAN and PFM algorithms significantly reduce localization errors and enhance environmental adaptability and robustness in both small and large simulated indoor environments. The findings confirm the robustness and efficiency of the proposed method in real-world indoor localization scenarios. In the inertial measurement unit (IMU)-based tracking algorithm, using the fingerprint database of initial coarse particles and the fingerprint database processed by the WGAN algorithm to locate the UAV, the localization error of the four algorithms is reduced by 30.3% on average. After using the PFM algorithm for matching localization, the localization error of the UAV is reduced by 28% on average.


    Access

    Download


    Export, share and cite



    Title :

    Research on 3D Localization of Indoor UAV Based on Wasserstein GAN and Pseudo Fingerprint Map


    Contributors:
    Junhua Yang (author) / Jinhang Tian (author) / Yang Qi (author) / Wei Cheng (author) / Yang Liu (author) / Gang Han (author) / Shanzhe Wang (author) / Yapeng Li (author) / Chenghu Cao (author) / Santuan Qin (author)


    Publication date :

    2024




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Research on Indoor Fingerprint Localization System Based on Voronoi Segmentation

    Li, Ang / Fu, Jingqi / Shen, Huaming | Springer Verlag | 2017


    DeFLoc: Deep Learning Assisted Indoor Vehicle Localization Atop FM Fingerprint Map

    Lei, Jiale / Huang, Junqin / Kong, Linghe et al. | IEEE | 2022



    Channel Covariance based Fingerprint Localization

    Li, Xinze / Al-Tous, Hanan / Hajri, Salah Eddine et al. | IEEE | 2024