This paper presents target tracking and target diving algorithms for Quadrotor-type Kamikaze UAVs (unmanned combat aerial vehicles, UCAVs). In the proposed approach, UAV's terminal attack dive analysis, aerodynamic features, and target dive physics flight are examined. A hybrid target tracking algorithm is developed using the Faster R-CNN ResNet101 model. The developed hybrid target tracking algorithm was observed to track targets with a 99.97% tracking accuracy and a frame rate of 156.89 FPS. In the Gazebo simulation environment, the UAV performs a dive maneuver at a constant speed in the x and z axes and a flight path angle of 53.13° to the targets it detects under dynamic environmental conditions.


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    Titel :

    Deep Learning Based Target Tracking and Diving Algorithm in Kamikaze UAVs


    Beteiligte:
    Can, Erdem (Autor:in) / Kirklar, Bedirye Ikbal (Autor:in) / Namdar, Mustafa (Autor:in) / Basgumus, Arif (Autor:in)


    Erscheinungsdatum :

    16.10.2024


    Format / Umfang :

    744252 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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