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<p>Unmanned aerial vehicles (UAVs) were originally developed for military applications, but their widespread adoption has also led to illegal uses, raising threats to national security and privacy. Consequently, the need for effective counter-drone technologies is rapidly increasing. Traditional counter-UAS approaches rely on target detection by radar on the ground, followed by soft-kill techniques like signal jamming. However, those methods often suffer from detection errors and tracking failures, limiting their practicality in diverse real-world conditions. To overcome such limitations, this paper proposes an onboard flight guidance algorithm that tracks and intercepts target drones by integrating gimbal camera information and vision-based target drone pose estimation. Unlike conventional guidance methods, the proposed approach utilizes target drone pose estimation to successfully intercept targets that maneuver up to 20% faster than the interceptor. We validate and analyze the performance of our method in physics-engine-based simulations under sky-like background conditions. The results demonstrate that, within these specified operational constraints, it intercepts target drones faster and more accurately than conventional guidance methods.</p>

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Flight Guidance Using Gimbal Tracking and Drone Pose Estimation for Interceptor Drones in Counter-Unmanned Aircraft Systems

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Hojun Lee,
  • Kyuman Lee

摘要

Unmanned aerial vehicles (UAVs) were originally developed for military applications, but their widespread adoption has also led to illegal uses, raising threats to national security and privacy. Consequently, the need for effective counter-drone technologies is rapidly increasing. Traditional counter-UAS approaches rely on target detection by radar on the ground, followed by soft-kill techniques like signal jamming. However, those methods often suffer from detection errors and tracking failures, limiting their practicality in diverse real-world conditions. To overcome such limitations, this paper proposes an onboard flight guidance algorithm that tracks and intercepts target drones by integrating gimbal camera information and vision-based target drone pose estimation. Unlike conventional guidance methods, the proposed approach utilizes target drone pose estimation to successfully intercept targets that maneuver up to 20% faster than the interceptor. We validate and analyze the performance of our method in physics-engine-based simulations under sky-like background conditions. The results demonstrate that, within these specified operational constraints, it intercepts target drones faster and more accurately than conventional guidance methods.