KEYWORDS: Data fusion, Data modeling, Systems modeling, Sensors, Intelligence systems, Data processing, Performance modeling, Control systems, Data mining, Data acquisition
This paper describes methods to affordably improve the robustness of distributed fusion systems by
opportunistically leveraging non-traditional data sources. Adaptive methods help find relevant data, create models,
and characterize the model quality. These methods also can measure the conformity of this non-traditional data with
fusion system products including situation modeling and mission impact prediction. Non-traditional data can
improve the quantity, quality, availability, timeliness, and diversity of the baseline fusion system sources and
therefore can improve prediction and estimation accuracy and robustness at all levels of fusion. Techniques are
described that automatically learn to characterize and search non-traditional contextual data to enable operators
integrate the data with the high-level fusion systems and ontologies. These techniques apply the extension of the
Data Fusion & Resource Management Dual Node Network (DNN) technical architecture at Level 4. The DNN
architecture supports effectively assessment and management of the expanded portfolio of data sources, entities of
interest, models, and algorithms including data pattern discovery and context conformity. Affordable model-driven
and data-driven data mining methods to discover unknown models from non-traditional and ‘big data’ sources are
used to automatically learn entity behaviors and correlations with fusion products, [14 and 15]. This paper describes
our context assessment software development, and the demonstration of context assessment of non-traditional data
to compare to an intelligence surveillance and reconnaissance fusion product based upon an IED POIs workflow.
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