Unmanned Aerial Vehicles (UAVs) are a highly innovative technology that is subject to strict regulations due to their potentially hazardous characteristics and a lack of legislative framework for their safe operation. To overcome these challenges, the Unmanned Air System Traffic Management (UTM) initiatives aim to establish validation and monitoring techniques for drone trajectories both prior to and during flight. In the UTM framework, drones will collaborate through systems similar to those used for ship and aircraft vehicles, such as Automatic Identification System (AIS) and Automatic Dependent Surveillance-Broadcast (ADSB). This paper presents an approach in the use of machine learning to gain insights into their kinematic behavior of UAV with the objective of detecting the drone airframe and classifying drones according to their characteristics.


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    Titel :

    UAV Airframe Classification Using Acceleration Spectrograms


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    García Bringas, Pablo (Herausgeber:in) / Pérez García, Hilde (Herausgeber:in) / Martínez de Pisón, Francisco Javier (Herausgeber:in) / Martínez Álvarez, Francisco (Herausgeber:in) / Troncoso Lora, Alicia (Herausgeber:in) / Herrero, Álvaro (Herausgeber:in) / Calvo Rolle, José Luis (Herausgeber:in) / Quintián, Héctor (Herausgeber:in) / Corchado, Emilio (Herausgeber:in) / Pedroche, David Sánchez (Autor:in)

    Kongress:

    International Conference on Soft Computing Models in Industrial and Environmental Applications ; 2023 ; Salamanca, Spain September 05, 2023 - September 07, 2023



    Erscheinungsdatum :

    31.08.2023


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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