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Exploring the state of the art deep learning algorithms in computer visionand voice recognition for applications in RF signal processing
Drones popularity has been rapidly increasing for a wide class of applications including commercial delivery, photography, fire-fighting and environmental monitoring due to their low cost and high commercial availability as unmanned aerial vehicles. In the light of this growth, anti-drone technology has become a significant research topic of investigation due to unauthorized flying of drones in sensitive airspace where their presence is regarded as a potential threat. In this project, we will modify and use existing well-established computer vision and voice recognition deep learning models for signal classification to remotely detect the presence of rogue drones in the air. We will analyze several datasets of radio signals transmitted by popular drones in the market to extract unique fingerprints hidden in the drone signals. Deep learning models will be designed to make use of the extracted fingerprints for detecting the presence of a drone signal.
Faculty Supervisor:
Ian Frigaard
Student:
Partner:
Skycope Technologies Inc
Discipline:
Engineering
Sector:
Information and cultural industries; Professional, scientific and technical services
University:
The University of British Columbia
Program:
Accelerate
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Mitacs is funded by the Government of Canada, the Government of Alberta, the Government of British Columbia, Research Manitoba, the Government of New Brunswick, the Government of Newfoundland and Labrador, the Government of Nova Scotia, the Government of Ontario, Innovation PEI, the Government of Quebec, the Government of Saskatchewan, and the Government of Yukon.