Maritime Seabed-to-Space Situational Awareness
FUNDING BODY: NATO ALLIED COMMAND TRANSFORMATION
The M-S3A project aims to improve NATO maritime situational awareness by integrating at pace information across domains—land, sea, air, space and cyber—through developing and demonstrating innovative artificial intelligence and information fusion (AI2F) concepts and technologies. Enhanced situational awareness is essential to maintain strategic advantage through ensuring the security and protection of critical infrastructure and maximising the effectiveness of NATO operations in the maritime domain.
Scientific research is carried out under this project along two strands.
- The first strand of research addresses advancing AI2F techniques for S3A from both a theoretical and an operational perspective, focusing on multi-domain data fusion through factor graphs, maritime anomaly detection, and the mathematical development of machine learning methodologies. In 2025, a novel deep-learning approach for processing passive acoustic data has been developed to detect and track vessels based on underwater acoustic signals, and the first at-sea operational tests of the system were performed in September 2025.
- The second strand exploits the research performed under the first strand and applies it to the operational aspects of monitoring and protecting critical undersea infrastructure (CUI). Work on deep-learning trajectory classification algorithms has improved the capability of CMRE’s Maritime Trajectory Classification (MARITRAC) tool to distinguish between relevant patterns and nominal vessel movements to reduce false alarm rates. Furthermore, research is being carried out to exploit sparse automatic identification system (AIS) data to indicate in real time whether a vessel could be dragging an anchor in the vicinity of CUI. The overall intent is to support the NATO Maritime Centre for the Security of CUI within the NATO Shipping Centre (NSC) at Allied Maritime Command (MARCOM)
