Hard and soft information fusion to aid situation understanding

该项目旨在设计和开发算法和方法，以识别和提取社交媒体和其他文档（软数据）中的信息，这些信息与使用机器学习、模式识别和其他新颖的非结构化数据挖掘方法等技术来理解特定领域的情况有关。该项目利用了帕特拉教授博士后的初步研究，m.o。St-Hilaire，后来加入OODA Technologies Inc. (OODA)，以及Patera教授和他的团队在李群理论的信息分析方面的广泛研究，以开发管理、分析和从软数据数据中提取知识的方法，并通过将这些信息与从物理传感器（硬数据）获得的其他信息/观察相融合，评估它们对情况理解的贡献。李群理论方法可以在计算速度、内存和硬件要求方面帮助提高多维数字数据处理性能。再加上工业合作伙伴OODA在开源智能（OSINT）信息处理方面的知识和专业知识，以及使用其产品的可能性，这种硬信息和软信息融合的新方法可以帮助设计新的工具来解决信息的可用性和质量问题，并提供必要的上下文来建立态势感知（SA）和理解，以帮助在任何领域以自动化的方式做出决策。科学贡献将在海事用例中进行演示和评估，以应对可能无法获得常用传感器数据或可能使用OSINT数据作为背景的情况。这可能会给加拿大北极地区的SA和理解带来潜在的好处，在那里OSINT数据可以帮助克服自动识别系统（AIS）和合成孔径雷达（SAR）传感器的挑战（例如信息质量、身份模糊、检测概率、刷新率和延迟）。蒙特利尔大学的科学团队将有机会直接为信息融合的最现代趋势做出贡献，并维护加拿大的主权。

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.