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Proceedings Paper

Affordable non-traditional source data mining for context assessment to improve distributed fusion system robustness
Author(s): Christopher Bowman; Gary Haith; Alan Steinberg; Charles Morefield; Michael Morefield
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Paper Abstract

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.

Paper Details

Date Published: 29 May 2013
PDF: 16 pages
Proc. SPIE 8756, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013, 875603 (29 May 2013); doi: 10.1117/12.2018386
Show Author Affiliations
Christopher Bowman, Data Fusion & Neural Networks, LLC (United States)
Gary Haith, Data Fusion & Neural Networks, LLC (United States)
Alan Steinberg, Georgia Tech Research Institute (United States)
Charles Morefield, Arctan Group LLC (United States)
Michael Morefield, Arctan Group LLC (United States)

Published in SPIE Proceedings Vol. 8756:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013
Jerome J. Braun, Editor(s)

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