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Proceedings Paper

Dual node decision wheels: an architecture for interconnected information fusion and decision making
Author(s): Amy Sliva; Joe Gorman; Christopher Bowman; Martin Voshell
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Paper Abstract

As the modern information environment continues to expand with new technologies, military Command and Control (C2) has increasing access to unprecedented amounts of data and analytic resources to support military decision making. However, with the increasing quantity and heterogeneity of multi-INT data—from new collection platforms, new sensors, and new analytic tools—comes a growing information fusion challenge. For example, increasingly distributed processing, exploitation, and dissemination (PED) capabilities and analyst intelligence resources must identify and integrate the most relevant data sources to support and improve operational command and control and situation awareness without becoming overwhelmed by data and potentially missing critical information. We present an innovative new information fusion and organizational decision-making architecture—Dual Node Decision Wheels (DNDW)—that integrates multi-INT PED, information analysis, and C2 processes through a novel combination of goal-directed information fusion and data-driven decision making, helping alleviate “big data” challenges through more fluid coordination of organizations and technologies. DNDW applies the dual node network for fusion and resource management with semantic links between organizational processes and decision aides, ensuring that each organizational role has access to the right information. DNDW can map fusion onto any organizational structure and provide a cost-effective solution methodology for integrating new technologies.

Paper Details

Date Published: 20 May 2015
PDF: 13 pages
Proc. SPIE 9464, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VI, 94640F (20 May 2015); doi: 10.1117/12.2176587
Show Author Affiliations
Amy Sliva, Charles River Analytics, Inc. (United States)
Joe Gorman, Charles River Analytics, Inc. (United States)
Christopher Bowman, Data Fusion and Neural Networks, LLC (United States)
Martin Voshell, Charles River Analytics, Inc. (United States)


Published in SPIE Proceedings Vol. 9464:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VI
Tien Pham; Michael A. Kolodny, Editor(s)

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