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Proceedings Paper

Measuring situational awareness and resolving inherent high-level fusion obstacles
Author(s): Moises Sudit; Adam Stotz; Michael Holender; William Tagliaferri; Kathie Canarelli
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Paper Abstract

Information Fusion Engine for Real-time Decision Making (INFERD) is a tool that was developed to supplement current graph matching techniques in Information Fusion models. Based on sensory data and a priori models, INFERD dynamically generates, evolves, and evaluates hypothesis on the current state of the environment. The a priori models developed are hierarchical in nature lending them to a multi-level Information Fusion process whose primary output provides a situational awareness of the environment of interest in the context of the models running. In this paper we look at INFERD's multi-level fusion approach and provide insight on the inherent problems such as fragmentation in the approach and the research being undertaken to mitigate those deficiencies. Due to the large variance of data in disparate environments, the awareness of situations in those environments can be drastically different. To accommodate this, the INFERD framework provides support for plug-and-play fusion modules which can be developed specifically for domains of interest. However, because the models running in INFERD are graph based, some default measurements can be provided and will be discussed in the paper. Among these are a Depth measurement to determine how much danger is presented by the action taking place, a Breadth measurement to gain information regarding the scale of an attack that is currently happening, and finally a Reliability measure to tell the user the credibility of a particular hypothesis. All of these results will be demonstrated in the Cyber domain where recent research has shown to be an area that is welldefined and bounded, so that new models and algorithms can be developed and evaluated.

Paper Details

Date Published: 18 April 2006
PDF: 9 pages
Proc. SPIE 6242, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006, 624205 (18 April 2006); doi: 10.1117/12.666756
Show Author Affiliations
Moises Sudit, SUNY, Univ. at Buffalo (United States)
Adam Stotz, Calspan-UB Research Ctr., Inc. (United States)
Michael Holender, SUNY, Univ. at Buffalo (United States)
William Tagliaferri, Alion Science and Technology (United States)
Kathie Canarelli, Alion Science and Technology (United States)

Published in SPIE Proceedings Vol. 6242:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006
Belur V. Dasarathy, Editor(s)

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