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This study presents the modeling and implementation of a fixed-wing UAV system with advanced avionics tailored for real-time target detection and tracking. The avionics system integrates NVIDIA Jetson Xavier NX hardware, optimized to run the YOLOv8x object detection algorithm, enabling precise and rapid detection of target UAVs. Additionally, a Long Short-Term Memory (LSTM) model is implemented to enhance the tracking capabilities by predicting target UAV trajectories based on historical flight data. The UAV system is designed to dynamically avoid no-fly zones using a pathfinding algorithm, ensuring mission compliance and safety. The detection and tracking systems are validated through a case study in a simulated environment, demonstrating the UAV’s ability to autonomously detect and track targets while adjusting its path to avoid restricted areas. This design marks a significant step forward in enhancing UAV operational autonomy, particularly in complex mission scenarios involving target tracking and airspace management.

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Avionics Design and Target Tracking for Intelligent Automated Fixed-Wing UAVs Using an LSTM Model

  • 会议论文

  • 发表日期：

    2025年

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  • Muhammed Nurullah Avran,
  • Murat Bakirci

摘要

This study presents the modeling and implementation of a fixed-wing UAV system with advanced avionics tailored for real-time target detection and tracking. The avionics system integrates NVIDIA Jetson Xavier NX hardware, optimized to run the YOLOv8x object detection algorithm, enabling precise and rapid detection of target UAVs. Additionally, a Long Short-Term Memory (LSTM) model is implemented to enhance the tracking capabilities by predicting target UAV trajectories based on historical flight data. The UAV system is designed to dynamically avoid no-fly zones using a pathfinding algorithm, ensuring mission compliance and safety. The detection and tracking systems are validated through a case study in a simulated environment, demonstrating the UAV’s ability to autonomously detect and track targets while adjusting its path to avoid restricted areas. This design marks a significant step forward in enhancing UAV operational autonomy, particularly in complex mission scenarios involving target tracking and airspace management.