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Proceedings Paper

Multisource taxonomy-based classication using the transferable belief model
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Paper Abstract

This paper addresses the problem of multi-source object classication in a context where objects of interest are part of a known taxonomy and the classication sources report at varying levels of specicity. This problem must consider several technical challenges: a) support fusion of heterogeneous classication inputs, b) provide a computationally scalable approach that accommodates taxonomy's with thousands of leaf nodes, and c) provide outputs that support tactical decision aides and are suitable inputs for subsequent fusion processes. This paper presents an approach that employs the Transferable Belief Model, Pignistic Transforms, and Bayesian Fusion to address these challenges.

Paper Details

Date Published: 10 May 2012
PDF: 7 pages
Proc. SPIE 8407, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012, 840704 (10 May 2012); doi: 10.1117/12.923873
Show Author Affiliations
William J. Farrell, Lakota Technical Solutions, Inc. (United States)
Andrew M. Knapp, Lakota Technical Solutions, Inc. (United States)


Published in SPIE Proceedings Vol. 8407:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012
Jerome J. Braun, Editor(s)

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