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Proceedings Paper

Information fusion and uncertainty management for biological multisensor systems
Author(s): Jerome J. Braun; Yan Glina; David W. Stein; Peter N. Skomoroch; Emily B. Fox
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Paper Abstract

This paper investigates methods of decision-making from uncertain and disparate data. The need for such methods arises in those sensing application areas in which multiple and diverse sensing modalities are available, but the information provided can be imprecise or only indirectly related to the effects to be discerned. Biological sensing for biodefense is an important instance of such applications. Information fusion in that context is the focus of a research program now underway at MIT Lincoln Laboratory. The paper outlines a multi-level, multi-classifier recognition architecture developed within this program, and discusses its components. Information source uncertainty is quantified and exploited for improving the quality of data that constitute the input to the classification processes. Several methods of sensor uncertainty exploitation at the feature-level are proposed and their efficacy is investigated. Other aspects of the program are discussed as well. While the primary focus of the paper is on biodefense, the applicability of concepts and techniques presented here extends to other multisensor fusion application domains.

Paper Details

Date Published: 28 March 2005
PDF: 12 pages
Proc. SPIE 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005, (28 March 2005); doi: 10.1117/12.605866
Show Author Affiliations
Jerome J. Braun, MIT Lincoln Lab. (United States)
Yan Glina, MIT Lincoln Lab. (United States)
David W. Stein, MIT Lincoln Lab. (United States)
Peter N. Skomoroch, MIT Lincoln Lab. (United States)
Emily B. Fox, MIT Lincoln Lab. (United States)

Published in SPIE Proceedings Vol. 5813:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005
Belur V. Dasarathy, Editor(s)

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