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Proceedings Paper

Hybrid methods for multisource information fusion and decision support
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Paper Abstract

This paper presents the progress of an ongoing research effort in multisource information fusion for biodefense decision support. The effort concentrates on a novel machine-intelligence hybrid-of-hybrids decision support architecture termed FLASH (Fusion, Learning, Adaptive Super-Hybrid) we proposed. The highlights of FLASH discussed in the paper include its cognitive-processing orientation and the hybrid nature involving heterogeneous multiclassifier machine learning and approximate reasoning paradigms. Selected specifics of the FLASH internals, such as its feature selection techniques, supervised learning, clustering, recognition and reasoning methods, and their integration, are discussed. The results to date are presented, including the background type determination and bioattack detection computational experiments using data obtained with a multisensor fusion testbed we have also developed. The processing of imprecise information originating from sources other than sensors is considered. Finally, the paper discusses applicability of FLASH and its methods to complex battlespace management problems such as course-of-action decision support.

Paper Details

Date Published: 18 April 2006
PDF: 12 pages
Proc. SPIE 6242, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2006, 624209 (18 April 2006); doi: 10.1117/12.664085
Show Author Affiliations
Jerome J. Braun, MIT Lincoln Lab. (United States)
Yan Glina, MIT Lincoln Lab. (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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