Tackling the Threat of Small Drones in Low Altitude Airspace over Metropolises

Project: Research

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Low and small (LS) drones are posing a pressing threat as they become increasingly accessible to reckless and malicious users through the commercial market. As the most widely used drone countermeasure, radio frequency (RF) jamming can disable drones' flight control by blocking their on-board pilot radios and RF sensors, therefore thwarting them from intruding into security and safety critical areas. However, existing drone jamming systems struggle to handle LS drones due to two key challenges. First, it is difficult to detect small drones and differentiate them from similar objects like birds under practical sensor resolution constraints. Second, it is highly challenging to reliably track and jam drones in complex low altitude airspace, especially for metropolis like Hong Kong where crowded buildings cause significant sensor clutters.   This project will develop a system called iHawk to tackle the threat of small drones in the low-altitude airspace over metropolises. We will develop a set of novel sensing and beamforming algorithms and systems based on the methodology of deeply fusing computer vision, radar, and array signal processing, enabling accurate detection of small drones under practical sensor resolution constraints, and reliable and efficient jamming against drones in complex low altitude airspace over metropolises. Motivated by the increasing deployments of smart lampposts as a key smart city infrastructure, we will further extend iHawk to harness the diverse sensing perspectives and distributed beamforming capabilities of smart lampposts to greatly improve the detection and jamming performance. The algorithms and systems will not only shed light on the design of countermeasure against next-generation fully autonomous drones, but also make an impact on a wide range of important Internet-of-Things applications that face similar challenges to that of LS drone detection and jamming, such as wireless localization, beamforming in vehicular networks, and environment monitoring in smart cities. 


Project number9043320
Grant typeGRF
Effective start/end date1/01/23 → …