Multi-source Aggregation (fusion) of Semantic Tiered Data (MAST)

Existing approaches for the multi-sensor data correlation for chemical and biological defense fail to provide timely and accurate situationalawareness capabilities in the presence of complex CBRN attack scenarios. Toward the maintenance of a common operating picture for situationalawareness, innovations are required to fuse signature feeds from networked chemical/biological (CB) and non-CB sensors in the presence oflimited bandwidth, identifying actionable threat intelligence about emergent threats within an operators area of responsibility. The Multi-sourceAggregation (fusion) of Semantic Tiered Data (MAST) approach is based on the development of comprehensive sensor semantic models.Included within the ontologies are uncertainty alignment models which assign context-aware uncertainty representations to sensor observations,accounting for errors, biases and variance in these observations. A tiered multi-source correlation methodology fuses CB/non-CB sensor feeds atlocal fusion nodes, including uncertainties within the fused output. Local fusion results are then with fused at higher echelons for regional andglobal threat alerting. Interfaces for the subscription to and visualization of threat-based alerts provide operator-context-centric commonoperational picture built on an existing fielded system. In addition to visualizing threats, the MAST interface enables the configuration of sensortipping and cueing logic leading to additional sensor readings which reduce threat uncertainties.