Grants and Contributions:

Title:
UAV-based magnetic anomaly detection system for remote sensing: design, build, test and fly (UAV-MAD)
Agreement Number:
DNDPJ
Agreement Value:
$750,400.00
Agreement Date:
Nov 8, 2017 -
Organization:
Natural Sciences and Engineering Research Council of Canada
Location:
British Columbia, CA
Reference Number:
GC-2017-Q3-00421
Agreement Type:
Grant
Report Type:
Grants and Contributions
Additional Information:

Grant or Award spanning more than one fiscal year (2017-2018 to 2021-2022).

Recipient's Legal Name:
Suleman, Afzal (University of Victoria)
Program:
Department of National Defence / NSERC Research Partnership - Project
Program Purpose:

Unmanned Aerial Vehicles (UAVs) have proven to be an integral instrument in a suite of defence and security capabilities for persistent intelligence, surveillance and reconnaissance, and civilian and industrial sector remote sensing applications including mining exploration and pipeline monitoring. Defence Research and Development Canada (DRDC) has determined that there is a need for research and development in the area of tactical UAVs to explore and exploit emerging capabilities in submerged vessel detection to ensure maritime domain awareness. On one hand, submarine designers have been researching methods and techniques to reduce detectable signatures such as acoustic emissions, and radar, infrared and visual signatures. For example, the very quiet modern diesel-electric submarines and operations in the shallow waters present acoustical detection challenges. On the other hand, new developments in Magnetic Anomaly Detection (MAD) sensors have been proposed as a potential and promising technology to complement the capabilities of acoustic sensors.x000D
Therefore, the development of a Canadian UAV based defence and security instrument of a MAD-equipped UAV for maritime domain awareness is proposed, and with potential applications to the civilian remote sensing sector. The proposed technology includes the design and development of performance optimized UAVs with long endurance and large area coverage capabilities, the design and development of a UAV-based MAD sensor , and the development and testing of network control algorithms for multi-UAV missions