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Proceedings Paper

Dempster-Shafer theory and Bayesian reasoning in multisensor data fusion
Author(s): Jerome J. Braun
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Paper Abstract

Bayesian and Dempster-Shafer Theory based methods are among the alternative algorithmic approaches to multisensor data fusion. The two approaches differ significantly and the extent of their applicability to data fusion is still being debated. This paper presents a Monte Carlo simulation approach for a comparative analysis of a Dempster-Shafer Theory based on a Bayesian multisensor data fusion in the classification task domain, including the implementation of both formalisms, and the results of the Monte Carlo experiments of this analysis.

Paper Details

Date Published: 3 April 2000
PDF: 12 pages
Proc. SPIE 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, (3 April 2000); doi: 10.1117/12.381638
Show Author Affiliations
Jerome J. Braun, MIT Lincoln Lab. (United States)

Published in SPIE Proceedings Vol. 4051:
Sensor Fusion: Architectures, Algorithms, and Applications IV
Belur V. Dasarathy, Editor(s)

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