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Contracting Agency: Ballistic Missile Defense Organization (BMDO)
Point of Contact: technology@kbsi.com

Overview

In a general sense, data fusion can be defined as "the set of methods, tools and means that uses data coming from various sources of different nature, in order to increase the quality (in a broad sense) of the requested information." At an intuitive level, data fusion, in taking more than one source of information into account, reflects the human perception and decision-making capabilities. A unified approach for designing decision support systems that support data fusion can make an enormous difference in the ease and efficiency of decision making. The approach for this SBIR effort involved the development of an intelligent adaptive data fusion system using soft computing paradigms including neural networks, fuzzy logic, and genetic algorithms.

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The key components of this approach included developing (i) a unified data representation scheme, (ii) a set of "data translators widgets" that help translate the raw data into the representation scheme, (iii) methodologies to iteratively develop intelligent data fusion systems based upon soft-computing approaches, (iv) methodologies to extract qualitative and quantitative knowledge out of the soft computing models, (v) methods that support multiple model integration, and (vi) experimentation and design rationale capture mechanisms.

The long term goal of this initiative was to establish innovative information processing technologies that advance the state of the art in interactive, computerized data fusion through the emerging biologically inspired learning systems such as Artificial Neural Networks, Fuzzy Logic, Expert Systems, and Genetic Algorithms.

 

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