Combining Cognitive Engineering and Information Fusion Architectures to Build Effective Joint Systems

Sliva1, A., Gorman1, J., Voshell1, M., Tittle1, J., and Bowman2, C.

Presented at SPIE Defense + Commercial Sensing, Baltimore Convention Center, Baltimore, MD (April 2016)

For effective decision making, military Command and Control (C2) requires robust situational understanding produced by translating multi-INT intelligence, surveillance, and reconnaissance (ISR) into actionable intelligence. These information products must be delivered according to the evolving decision needs of dynamic mission contexts. However, traditional ISR pipelines rely upon highly distributed analysis and processing, exploitation, and dissemination that reduce coordination between operators, analysts, and Commanders. The Dual Node Decision Wheels (DNDW) architecture was previously described as a novel approach toward integrating analytic and decision-making processes. In this paper, we extend DNDW with a technical description of components in this framework, combining structures of the Dual Node Network (DNN) for Information Fusion and Resource Management with a variation on Rasmussen’s Decision Ladder to provide guidance on constructing information systems that better serve decision-making requirements.

The DNN takes a component-centered approach to system design, decomposing each asset in terms of data inputs and outputs according to their intended roles and interactions in a fusion network. However, to ensure relevancy to C2 processes, principles from cognitive systems engineering dictate that system design should also take a human-centered view, with information needs driving the architecture and capabilities of network assets. We present a new node structure for DNDW that uses a unique hybrid DNN top down system design and human-centered process design, combining DNN node decomposition with artifacts from cognitive analysis (i.e., system abstraction decomposition models, decision ladders) to provide ISR work domain and task-level insights at different levels of the system. This structure will ensure not only that the information fusion technologies and processes are structured effectively, but that the resulting information products will align with the requirements of human decision makers.
1 Charles River Analytics
2 Data Fusion Corp.

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