The accuracy of localization using global positioning system (GPS) data plays a key role in reliable positioning and control of unmanned aerial vehicles (UAVs). This paper proposes a novel statistically-aided earning-based localization approach, called filtered neural network (FNN) for high-precision localization of UAVs. The proposed FNN framework utilizes an entropy adaptive Kalman filter to fine-tune the inputs to a recurrent neural network, which works in a loop with the filter to generate subsequent robust position estimates. The proposed framework outperforms the state-of-the-art techniques with an nRMSE of ≈ 10−6, ≈ 97% reduced estimation delay, ≈ 73% reduced modeling time, ≤ 100 lag samples for FNN training, and only 4-6 overall model retraining instances per flight trajectory. The results are verified over a wide range of mean GPS noise power.


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    Titel :

    A Novel Statistically-Aided Learning Framework for Precise Localization of UAVs


    Beteiligte:
    Mandal, Akash Kumar (Autor:in) / Seo, Jun-Bae (Autor:in) / De, Swades (Autor:in) / Poddar, Ajay K. (Autor:in) / Rohde, Ulrich (Autor:in)


    Erscheinungsdatum :

    01.06.2023


    Format / Umfang :

    1417996 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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