17 May 2016 Information fusion for the Gray Zone
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Abstract
United States Special Operations Command (SOCOM) recently published a white paper describing the “Gray Zone”, security challenges characterized by “ambiguity about the nature of the conflict, opacity of the parties involved…competitive interactions among and within state and non-state actors that fall between the traditional war and peace duality.”1 Ambiguity and related uncertainty about actors, situations, relationships, and intent require new approaches to information collection, processing and fusion. General Votel, the current SOCOM commander, during a recent speech on “Operating in the Gray Zone” emphasized that it would be important to get left of the next crises and stated emphatically, “to do that we must understand the Human Domain.”2 This understanding of the human domain must come from making meaning based on different perspectives, including the “emic” or first person/participant and “etic” or third person/observer perspectives. Much of the information currently collected and processed is etic. Incorporation and fusion with the emic perspective enables forecasting of behaviors/events and provides context for etic information (e.g., video).3 Gray zone challenges are perspective-dependent; for example, the conflict in Ukraine is interpreted quite differently by Russia, the US and Ukraine. Russia views it as war, necessitating aggressive action, the US views it as a security issue best dealt with by economic sanctions and diplomacy and the Ukraine views it as a threat to its sovereignty.4 General Otto in the Air Force ISR 2023 vision document stated that Air Force ISR is needed to anticipate strategic surprise.5 Anticipatory analysis enabling getting left of a crisis inherently requires a greater focus on information sources that elucidate the human environment as well as new methods that elucidate not only the “who’s” and “what’s”, but the “how’s and “why’s,” extracting features and/or patterns and subtle cues useful for forecasting behaviors and events; for example discourse patterns related to social identity and integrative complexity.6 AFRL has been conducting research to enable analysts to understand the “emic” perspective based on discourse analysis methods and/or text analytics.7 Previous results demonstrated the value of fusion of emic and etic information in terms of improved accuracy (from 39% to 86%) in forecasting violent events.8 This paper will describe new work to extend this to anticipatory analysis in the gray zone.
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Laurie Fenstermacher, Laurie Fenstermacher, } "Information fusion for the Gray Zone", Proc. SPIE 9842, Signal Processing, Sensor/Information Fusion, and Target Recognition XXV, 98421M (17 May 2016); doi: 10.1117/12.2230993; https://doi.org/10.1117/12.2230993
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