in Defence

BAE Systems advanced analytics to detect WMD threats

Posted 13 February 2020 · Add Comment

BAE Systems has received funding from the US Defense Advanced Research Projects Agency's (DARPA) Defense Sciences Office to develop advanced analytics technology that will assist in the detection and deterrence of Weapons of Mass Destruction (WMD) activity, helping to ensure the USA's national security.



Above: BAE Systems is to develop advanced analytics technology to assist in the detection and deterrence of mass destruction activity.
Courtesy BAE Systems


The first-of-its-kind technology will leverage multiple data sources and uses data fusion, adversary modelling, pattern matching and machine learning techniques to detect and identify indications of chemical, biological, radiological, nuclear and explosive (CBRNE) threat.

As part of DARPA's SIGMA+ programme, the BAE Systems FAST Labs research and development team will work with partners Barnstorm Research and Washington State University to create a technology solution called MATCH (Multi-info Alerting of Threat CBRNE Hypotheses). MATCH will automatically populate a world graph using sensor and multi-source data to provide analysts visibility into threat activities in a metropolitan region. Using the graph, MATCH will create hypotheses that identify and characterise threatening CBRNE activity.

"Our technology aims to help analysts close the loop between the analysis of information and the collection of new information to fill in the gaps and provide a comprehensive picture of a potential threat," said Chris Eisenbies, product line director of the Autonomy, Controls, and Estimation group at BAE Systems. "Most importantly, our solution automates a process that is currently manually intensive, improving an analyst’s ability to quickly and accurately identify CBRNE activity and ultimately, helping to protect our country from these significant dangers."

Phase 1 research on the SIGMA+ programme leverages BAE Systems' expertise in data fusion, advanced analytics and resource management as part of its autonomy technology portfolio. It also builds on a previous work for DARPA's Insight programme and leverages the company's mature All-Source Track and Identity Fuser (ATIF) and Multi-INT Analytics for Pattern Learning and Exploitation (MAPLE) technologies. Work for the programme will be completed at the company's facilities in Burlington, Massachusetts and Arlington, Virginia.

 

 

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