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

David Cheng; Grant Nicol

doi:10.1117/12.2663622

14 June 2023 An air-to-air unmanned aerial vehicle interceptor using machine-learning methods for detection and tracking of a target drone

David Cheng, Grant Nicol

Author Affiliations +

Proceedings Volume 12549, Unmanned Systems Technology XXV; 125490D (2023) https://doi.org/10.1117/12.2663622
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

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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David Cheng and Grant Nicol "An air-to-air unmanned aerial vehicle interceptor using machine-learning methods for detection and tracking of a target drone", Proc. SPIE 12549, Unmanned Systems Technology XXV, 125490D (14 June 2023); https://doi.org/10.1117/12.2663622

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KEYWORDS

Object detection

Cameras

Target detection

Education and training

Video

Infrared cameras

Unmanned aerial vehicles

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