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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    Title :

    Deep Learning Based Target Tracking and Diving Algorithm in Kamikaze UAVs


    Contributors:


    Publication date :

    2024-10-16


    Size :

    744252 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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