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23 April 2020 Jellyfish: non-radio frequency counter drone technology
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In contrast of RF jamming devices for counter-UAV measure, which proves ineffective for autonomous UAV, we propose Jellyfish - a vision-based and machine learning driven defensive approach using inexpensive drones. Jellyfish minimizes cost while maximizing the effectiveness of countering autonomous UAV threats. In software aspect, we collect synthetic and real-world imagery and train deep learning model for real-time adversarial drone detection and tracking and then feed tracking output to a highly responsive controller to mirror the movement of the adversary. On the other hand, we design a lightweight and self-releasable capturing mechanism, similar to the tentacles of a jellyfish (hence the name “Jellyfish"), to optimize both the capturing area and defending drone's maneuverability and survivability.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Peng Zhang, Lance Lowenberg, Michael Tall, and Michael Ouimet "Jellyfish: non-radio frequency counter drone technology", Proc. SPIE 11425, Unmanned Systems Technology XXII, 114250E (23 April 2020);

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