Paper
5 May 2011 Information fusion measures of effectiveness (MOE) for decision support
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Abstract
For decades, there have been discussions on measures of merits (MOM) that include measures of effectiveness (MOE) and measures of performance (MOP) for system-level performance. As the amount of sensed and collected data becomes increasingly large, there is a need to look at the architectures, metrics, and processes that provide the best methods for decision support systems. In this paper, we overview some information fusion methods in decision support and address the capability to measure the effects of the fusion products on user functions. The current standard Information Fusion model is the Data Fusion Information Group (DFIG) model that specifically addresses the needs of the user in an information fusion system. Decision support implies that information methods augment user decision making as opposed to the machine making the decision and displaying it to user. We develop a list of suggested measures of merits that facilitate decision support decision support Measures of Effectiveness (MOE) metrics of quality, information gain, and robustness, from the analysis based on the measures of performance (MOPs) of timeliness, accuracy, confidence, throughput, and cost. We demonstrate in an example with motion imagery to support the MOEs of quality (time/decision confidence plots), information gain (completeness of annotated imagery for situation awareness), and robustness through analysis of imagery over time and repeated looks for enhanced target identification confidence.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Erik P. Blasch, Richard Breton, and Pierre Valin "Information fusion measures of effectiveness (MOE) for decision support", Proc. SPIE 8050, Signal Processing, Sensor Fusion, and Target Recognition XX, 805011 (5 May 2011); https://doi.org/10.1117/12.883988
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Cited by 48 scholarly publications.
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KEYWORDS
Decision support systems

Data modeling

Information fusion

Cognitive modeling

Sensors

Systems modeling

Molybdenum

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