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Proceedings Paper

Interactive analysis of situational awareness metrics
Author(s): Derek Overby; Jim Wall; John Keyser
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Paper Abstract

Digital systems are employed to maintain situational awareness of people in various contexts including emergency response, disaster relief, and military operations. Because these systems are often operated in wireless environments and are used to support real-time decision making, the accuracy of the SA data provided is important to measure and evaluate in the development of new systems. Our work has been conducted in conjunction with analysts in the evaluation and performance comparison of different systems designed to provide a high degree of situational awareness in military operations. To this end, we defined temporal and spatial metrics for measuring the accuracy of the SA data provided by each system. In this paper we discuss the proposed temporal and spatial metrics for SA data and show how we provided these metrics in a linked coordinated multiple view environment that enabled the analysts we worked with to effectively perform several critical analysis tasks. The temporal metric is designed to help determine when network performance has a significant effect on SA data, and therefore identify specific time periods in which individuals were provided inaccurate position data for their peers. Temporal context can be used to determine the local or global nature of any SA data inaccuracy, and the spatial metric can then be used to identify geographic effects on network performance of the wireless system. We discuss the interactive software implementation of our metrics and show how this analysis capability enabled the analysts to evaluate the observed effects of network latency and system performance on SA data during an exercise.

Paper Details

Date Published: 24 January 2012
PDF: 12 pages
Proc. SPIE 8294, Visualization and Data Analysis 2012, 829406 (24 January 2012); doi: 10.1117/12.905187
Show Author Affiliations
Derek Overby, Texas A&M Univ. (United States)
Jim Wall, Texas A&M Univ. (United States)
John Keyser, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 8294:
Visualization and Data Analysis 2012
Pak Chung Wong; David L. Kao; Ming C. Hao; Chaomei Chen; Robert Kosara; Mark A. Livingston; Jinah Park; Ian Roberts, Editor(s)

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