Mr. Grant Nicol Profile

Grant Nicol

at DRDC Suffield

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Proceedings Article | 14 June 2023 Paper

An air-to-air unmanned aerial vehicle interceptor using machine-learning methods for detection and tracking of a target drone

David Cheng, Grant Nicol

Proceedings Volume 12549, 125490D (2023) https://doi.org/10.1117/12.2663622

KEYWORDS: Object detection, Cameras, Target detection, Education and training, Video, Infrared cameras, Unmanned aerial vehicles, Machine learning, Infrared radiation, Global Positioning System

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The proliferation of small or micro unmanned aerial vehicles (UAV) gives rise to a potential threat for both public and military security. The small footprint and unpredictable dynamics of drones make detection and tracking difficult. Traditional methods of defence and protection may be ineffective against this new danger. This paper presents the work on developing DroneSwatter, a counter unmanned aerial system developed to track, follow, and take down a drone threat (Target Drone) using an agile, low-cost drone interceptor (Hunter Drone). The DroneSwatter project aims to apply machine learning techniques for counter-drone scenarios. Detection tasks are performed using deep learning detection algorithms. Simulation is used to build a tracking control model via proportional-derivative (PD) and machine learning algorithms. Optical pursuit based on images collected from the onboard camera of a Hunter Drone is implemented to track a Target Drone. Field experiments were conducted to test the feasibility and functionality of the current software and hardware methods for the DroneSwatter system. A benchmark was established by flying a target drone in designed patterns and the performance of the DroneSwatter tracking system was evaluated based on what speeds the Hunter Drone could follow the Target Drone in the field testing.

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