5 May 2009 An extreme events laboratory to provide network centric collaborative situation assessment and decision making
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Abstract
Rapid improvements in communications infrastructure and sophistication of commercial hand-held devices provide a major new source of information for assessing extreme situations such as environmental crises. In particular, ad hoc collections of humans can act as "soft sensors" to augment data collected by traditional sensors in a net-centric environment (in effect, "crowd-sourcing" observational data). A need exists to understand how to task such soft sensors, characterize their performance and fuse the data with traditional data sources. In order to quantitatively study such situations, as well as study distributed decision-making, we have developed an Extreme Events Laboratory (EEL) at The Pennsylvania State University. This facility provides a network-centric, collaborative situation assessment and decision-making capability by supporting experiments involving human observers, distributed decision making and cognition, and crisis management. The EEL spans the information chain from energy detection via sensors, human observations, signal and image processing, pattern recognition, statistical estimation, multi-sensor data fusion, visualization and analytics, and modeling and simulation. The EEL command center combines COTS and custom collaboration tools in innovative ways, providing capabilities such as geo-spatial visualization and dynamic mash-ups of multiple data sources. This paper describes the EEL and several on-going human-in-the-loop experiments aimed at understanding the new collective observation and analysis landscape.
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Brian J. Panulla, Loretta D. More, Wade R. Shumaker, Michael D. Jones, Robert Hooper, Jeffrey M. Vernon, Stanley G. Aungst, "An extreme events laboratory to provide network centric collaborative situation assessment and decision making", Proc. SPIE 7305, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VIII, 73050W (5 May 2009); doi: 10.1117/12.818751; https://doi.org/10.1117/12.818751
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Sensors

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Data modeling

Visualization

Data fusion

Information technology

Statistical analysis

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