Grants and Contributions:

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Title:

Hard and soft information fusion to aid situation understanding

Agreement Number:

CRDPJ

Agreement Value:

$173,920.00

Agreement Date:

Feb 7, 2018 -

Organization:

Natural Sciences and Engineering Research Council of Canada

Location:

Quebec, CA

Reference Number:

GC-2017-Q4-01422

Agreement Type:

Grant

Report Type:

Grants and Contributions

Additional Information:

Grant or Award spanning more than one fiscal year (2017-2018 to 2019-2020).

Recipient's Legal Name:

Patera, Jiri (Université de Montréal)

Program:

Collaborative Research and Development Grants - Project

Program Purpose:

The aim of this project to design and develop algorithms and methods to identify and extract information from social media and other documents (soft data), which are pertinent to situation understanding in a specific domain by using techniques such as machine learning, pattern recognition and other novel unstructured data mining approaches. This project leverages the initial study by Prof. Patera's Postdoc, M.-O. St-Hilaire, who later joined OODA Technologies Inc. (OODA) as well as Prof. Patera's and his team's extensive research in information analysis in the theory of Lie groups to develop approaches for management, analysis and extraction of knowledge from soft data data and assess their contribution into situation understanding, through fusion of this information with other information/observations obtained from physical sensors (hard data). The Lie group theoretical approach can help improve multi-dimensional digital data processing performance in terms of computational speed, and memory and hardware requirements. Together with knowledge and expertise of the industrial partner OODA in Open Source Intelligence (OSINT) information processing and the possibility to use their products, this novel approach to hard and soft information fusion can help design novel tools for addressing availability and quality of information as well as bringing the necessary context to establish situation awareness (SA) and understanding to aid decision making in an automated way in any domain. The scientific contributions will be demonstrated and evaluated on the maritime use case for scenarios where commonly used sensor data may not be available or where OSINT data may be used as context. This could bring potential benefit to SA and understanding in the Canadian Arctic where OSINT data could help overcome Automated Identification System (AIS) and Synthetic Aperture Radar (SAR) sensor challenges (e.g.information quality, identity ambiguity, probability of detection, refresh rate and latency). The scientific team at the University of Montreal will have the opportunity to contribute directly to the most modern trend of information fusion as well as to maintaining Canadian sovereignty.x000D
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