Monday, November 20, 2017
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CMRE supports the enhancement of the Maritime Situational Awareness interoperability in NATO

MISC 400From 19 to 21 November 2014 the Centre hosted at its premises the Fall 2014 Maritime Information Services Conference (MISC).

The MISC is a formally constituted forum with the NATO Maritime Information Service (MIS) Community of Interest (COI). The aim of the MISC is delivering a modern Maritime Command and Control (C2), which means contributing a high quality Recognized Maritime Picture (RMP) to NATO's situational awareness and Common Operational Picture (COP). This forum continuously involves the MIS COI in the management of the Maritime Command and Control Information System (MCCIS), the Maritime Situational Awareness (MSA) Prototype and Future Maritime Information Services (FMIS) to be implemented as Project TRITON (the NATO funded implementation programme to replace MCCIS). In particular the MSA component is essential to create and monitor a comprehensive picture of maritime movements and to share this information within NATO in order to enable better collective and/or individual responses.

The MISC, chaired by Allied Command Transformation (ACT), consists of representatives from NATO Nations and NATO Commands and organizations. The Conference at CMRE was attended by approximately 80 personnel from Nations and NATO entities (Allied Command Transformation, Allied Command Operations, NCIA, MARCOM) and Industry, and addressed issues related to current and future Maritime C2 Information Services.

As a prominent player in delivering maritime solutions, CMRE proved to be an ideal venue for the meeting. The Centre daily contributes to NATO's vision for the future looking at specific tools, technologies, and techniques in support of Maritime Situational Awareness (MSA) to develop enhanced Maritime C2 capabilities. CMRE's scientists have developed data-driven sensor performance models using big data techniques for information products which synthesize otherwise overwhelming amounts of information into concise layers which can be understood and actioned. Traffic Route Extraction and Anomaly Detection (TREAD) model uses historical data to learn vessel behavior and synthesize these behaviors as traffic routes. The synthetic routes are characterized statistically to create the ability to identify vessels which deviate from normal traffic behavior.

 

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