Our services are geared toward helping our clients derive the most knowledge out of their data and information so they can focus on timely, productive decision-making. We achieve this with a scientific approach, backed by more than 20 years of experience in decision-making support, and a seasoned, results-centred defence pedigree.
Data fusion is the process of combining data to refine state estimates and predictions.
In practice, the goal is for the final picture to provide more meaningful information than the sum of its parts and a better understanding of the meaning and evolution of this picture in the context of the observer's goals. Although Fusion originated as an applied defence process, the tools developed and the ground already covered, intersect with many application fields, and we have the proven experience to efficiently apply the technology with palpable results.
OODA is a leader in the field of fusion, chairing the Board of Directors of the International Society for Information Fusion (ISIF), organizing studies and workshops, and promoting its use.
 Steinberg, A.N., Bowman, C.L., and White, F.E.,“Revisions to the JDL Data Fusion Model”, in Sensor Fusion: Architectures, Algorithms, and Applications, Proceedings of the SPIE, Vol. 3719, 1999
An essential element of knowledge discovery is to unearth unknown models of activity or behaviour from large volumes of data.
With products under its belt like Bullbear and Marketsense, OODA is particularly competent in performing natural language processing on vast quantities of documents and massive databases to extract meaningful information.
OODA builds large relational databases that acquire data from multiple sources in real-time and are easily extensible. We have pushed the speed limits of PostgreSQL centralized databases allowing them to comfortably handle terabytes of data.
A strong database architecture is often the backbone of data analysis and OODA has the capability to tailor it to our client’s specific needs.
OODA’s experience extends to producing clear visual representations of data allowing organizations to see the overall picture, often making use of geographic information systems (GIS) to provide more insight into data.
Decision support systems provide automated support to human decision making processes to facilitate and accelerate the decision cycles.
Automated decision support technologies are increasingly being integrated in military command and control (C2) systems and we have actively participated in that effort since our beginnings. We also apply our strong expertise in military decision support to diverse domains such as: finance, urban planning, pollution emergency management, etc.
At OODA, we believe that true understanding is a process of unwearying refinement, and the willingness to pursue the result to its fruition. This view is part of our work ethic, our projects and our name.
Elisa is a subject matter expert in the areas of data & information fusion, command and control systems, tactical datalinks and decision support systems. She does business development nationally and internationally identifying opportunities for project pursuits in decision support technologies.
Elisa is very active in national and international organizations involved in and promoting R&D in data/information fusion, decision support and applied mathematics: 2009 Chair and Member of the Board of Directors of the International Society for Information Fusion (ISIF)(2002-current); organized NATO Science for Peace study institutes and workshops and supported NATO Research and Technology Organisation (RTO) events (2000-current); Associate Member of the Centre de Recherches Mathématiques (CRM) at Université de Montréal (1995-current); and has been member of the Board of Directors (2000-2010) and Research Management Committee (2000-2011) for Mathematics of Information Technology and Complex Systems (MITACS) Network Centre of Excellence (NCE), Canada.
Marie-Odette is an expert in the application of mathematics and statistics to solve operational problems in diverse sectors including, but not limited to, maritime surveillance, finance, social networks and intelligence. Marie-Odette is manager and technical lead on several projects related to the exploitation of maritime information, including the development of an infrastructure to manage maritime real-time big data. She is also the co-creator of BullBear.
Eric is a maritime domain awareness, data fusion, sensor systems and systems engineering specialist. Eric has many years of experience in project management from capture to closure. He was also responsible for developing the C4ISR segment of an R&D group, including such tasks as: participating actively in proposal writing, building strong relationships with external customers and presenting at national and international events.
Yannick is an expert in algorithm development for information fusion and satellite image analysis as well as in the design and implementation of image processing, information fusion and decision support systems. He was technical lead and project manager on image analysis and information fusion projects funded by the Canadian Space Agency. Yannick was lead developer int the team that previously developed the first Canadian application segment for the US Common Operating Environment, which was successfully subjected to independent verification and validation by the Space and Naval Warfare Systems Command.
Michel is a Jack-of-all-trades in the best sense of the word. From network administration to large database modelling, Michel can investigate, analyze and solve problems in various technological and R&D areas. A very quick and resourceful learner, he can even tackle situations in very recent scientific fields. Michel's past and still ongoing experience includes system architecture, multi-agent and blackboard frameworks, distributed computing, software architecture, data fusion, sensor modeling, simulation and systems engineering. He is particularly interested in AI technology and data mining.
Dan is a seasoned software designer and an expert in big data and large network visualization and analysis. He has applied his knowledge in flexible framework design and large database management systems in the maritime surveillance domain where ship data from all over the world can be merged and stored in a single format. He contributed to the final push on the Halifax Class Modernization program at Lockheed Martin with work on data fusion database alignment as well as user interface development. He also developed several applications for social network analysis and visualization.
Mathieu Lavallée is a dynamic full-stack engineer, expert in web development and in data visualization. Mathieu is a fast learner able to adapt to ever-changing situation, but also identify and troubleshoot critical issues. From requirement analysis to deployment or from database to user interface, Mathieu will get the job done having the big picture in mind. Among others, he created an interactive web application for geomarketing data visualization.
The volume of maritime vessel data, such as available from the Automatic Identification System (AIS), places considerable burden on systems designed and developed to manage data pertaining to maritime traffic. A properly designed and implemented data management infrastructure can provide benefit to the maritime domain awareness research community, by supporting data volumes, diverse user needs, and product management. Such an infrastructure has been constructed on a modest budget by utilizing open-source technologies. This paper describes the Maritime Situational Awareness Research Infrastructure (MSARI), and the design of the underlying database to meet data volume and user analysis needs. The resulting infrastructure currently handles input rates of approximately two billion vessel reports per month. This work is of potential benefit to those in the navigational community interested in the long-term storage and usage of global vessel data such as available from AIS.
Moo, P., Kashyap, N., Ponsford, T., McKerracher, R., DiFilippo, D., Allard, Y., Canada's Third Generation High Frequency Surface Wave Radar (HFSWR) System for Persistent Surveillance of the EEZ, The Journal of Ocean Technology, Vol. 10, No. 2, pages 22-28. 2015.
The granting of coastal nations’ sovereign rights over their 200 nautical miles (nm), Exclusive Economic Zone (EEZ) established the requirement for persistent surveillance. The need to provide continuous surveillance of shipping activity out to and beyond the EEZ led to a reassessment of High Frequency Surface Wave Radar (HFSWR). Canada has been investigating the use of HFSWR for persistent surveillance of the EEZ for more than 20 years. Early development cumulated in 2003 with the deployment of two SWR-503 HFSWR systems for monitoring the economically significant Grand Banks region on Canada’s east coast. Raytheon Canada Limited (RCL) subsequently sold a number of SWR- 503 systems to international customers; however, operation of the Canadian systems was terminated in 2007 due to concerns related to the potential for the systems to cause interference to HF communication users. The international systems continue to remain in operation.
Shahbazian, E., Hadzagic, M., Isenor, A., Allard, Y., Processing and Fusion of Unmanned Underwater Vehicle Information for Maritime Surveillance, NATO IST-SET-126 Symposium on "Information Fusion (Hard and Soft) for ISR", Norfolk, Virginia, 4-5 May, 2015
Numerous maritime surveillance applications can benefit from the use of Unmanned Underwater Vehicles (UUV)s performing various roles as they offer persistence and data quality that may not be achievable using traditional methods, especially in remote underwater environments where deploying personnel is very costly or not feasible. However, the underwater information collection and exchange, which is done mostly via acoustic and, to a lesser extent, optical means is more challenging than in the above water domain, leading to larger uncertainties and latencies. Therefore, the UUV types, the underwater information collection configuration, the specific sensor payloads as well as the architecture for integration of the UUV observations into the fusion system that builds the situational picture need to be designed so as to efficiently and reliably support the requirements of the specific application. This paper provides a high level summary of how UUV collected data and information may be processed, shared and integrated to describe the overall maritime situation, thereby providing improved knowledge and improved Maritime Domain Awareness (MDA) using a use-case scenario typical to the Canadian north. It describes processing approaches pertinent in such surveillance applications and also discusses challenges and approaches for addressing the issues with fusion of this information within the surface fusion system.
Isenor, A., Allard, Y., Mayrand, M, Comparing Information Structures Used in the Maritime Defence and Security Domain, Proceedings of NATO Advanced Research Workshop, Aghveran, Armenia, 1-5 June, 2015, IOS Press, In Publication
As assets are engaged to collect data and intelligence on the entities or parties involved in a defence or security operation, much of the collected data are represented in electronic form and subsequently stored in an assortment of files or databases. It is also typical that these data or information need to be shared with others involved in the investigation, task group, coalition, or security force. The effectiveness of this sharing is predominantly judged by how well the receiving party understands the information it has received. In this paper we examine the commonality of concepts between information systems typical of the Canadian defence and security regime. Specifically we consider components of the National Information Exchange Model (NIEM), the Canadian Naval Positioning Repository (NPR), a port clearance structure, and a messaging structure that evolved out of a Canadian defence research and development effort. The information structures, or schemas, are examined using the open-source software for combining match algorithms (COMA). COMA provides a user guided automated method to combine multiple schemas using a variety of matcher algorithms, including an approach that reuses results from previous match operations, and combines the results of multiple match operations. Results of the investigation show how the non-specific nature of the maritime defence and security topic space introduce vocabulary complexities. For example, the non-specific aspects of naming like vessel, ship, and boat. Results also show how discrepancies are introduced through data typing, the structure itself, and semantics.
Meeting Security Challenges Through Data Analytics and Decision Support, Shahbazian, E., Rogova, G. (Eds.) , Proceedings of NATO Advanced Research Workshop, Aghveran, Armenia, 1-5 June, 2015, NATO Science for Peace and Security Series, IOS Press, in publication.
The Advanced Research Workshop (ARW) on the topic of " Meeting Security Challenges Through Data Analytics and Decision Support" to ascertain problems and explore technological solutions using data analytics and decision support to meet emerging security challenges in three of the NATO priority areas of common interest with Partners countries. The purpose of an Advanced Research Workshop is to contribute to the critical assessment of existing knowledge on new important topics, to identify directions for future research, and to promote close working relationships between scientists from different countries and with different professional experience.
Human operators trying to establish individual or collective maritime situational awareness often find themselves overloaded by huge amounts of information obtained from multiple and possibly dissimilar sources. This paper explores potential use of open source data mining tools, in particular R software, to enable discovery of maritime traffic patterns. It also presents an assessment of R software as a data mining tool using spatio-temporal maritime traffic data such as from the Automatic Identification System (AIS), and includes scenarios of potential interest to the maritime environment.
Shahbazian E., Pigeon L., Kraft J., Bosse E., Building Technology Enabled Decision Support Applications, Proceedings of NATO Science for Peace and Security Series E: Human and Societal Dynamics - Vol. 109, IOS Press, The Netherlands, Mar. 2013
Development of advanced technology (e.g. High Level Information Fusion (HLIF), Data Mining, Artificial Intelligence (AI), human factors, etc.) enabled decision support for prediction and recognition of piracy activity is an extremely challenging problem. It requires the consideration of multiple interrelated factors and constraints for numerous aspects of the system, including legal, jurisdictional, information sharing and exchange, information availability and quality, user roles and hierarchy, application of hardware and software technologies, etc. which do not lend themselves to simple analyses. There are numerous stakeholders, and no one has either an understanding of, or access to, the entire knowledge to be able to resolve any of these challenges by themselves. There are too many unknowns for a traditional top-down design methodology to be feasible to use. Because scenarios are task-based and descriptive, it was judged that the Scenario Based Design (SBD) approach needs to be explored for advanced technology enabled systems where the human is part of the system. This chapter presents an initial analysis of how SBD methodology could be applied to incrementally develop advanced decision support capabilities for prediction and recognition of piracy activity in collaboration with the stakeholders.