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Proceedings Paper

Fuzzy causal probabilistic networks and multisensor data fusion
Author(s): HePing Pan; Nickens N. Okello; Daniel W. McMichael; Matthew Roughan
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Paper Abstract

This paper presents the theory and formalism of fuzzy causal probabilistic networks (FCPN) and show their current and potential applications in multisensor data fusion. A FCPN is a directed acyclic graph representing the joint probability distributions of a set of fuzzy random variables describing a problem domain. FCPNs extend causal probabilistic networks, also called Bayesian networks, belief networks, or influence diagrams, by associating each discrete variable with a fuzzifier and a defuzzifier, if required. A fuzzifier converts a crisp variable to a fuzzy discrete variable while a defuzzifier does the inverse. FCPNs provide a high-level generic architecture for fusing data incoming from multiple sensors. The paper also provides an overview on the field of multisensor data fusion. Airborne early warning and control using multiple sensors is studied to showcase the theory of FCPNs and their applications for multisensor data fusion.

Paper Details

Date Published: 25 September 1998
PDF: 12 pages
Proc. SPIE 3545, International Symposium on Multispectral Image Processing (ISMIP'98), (25 September 1998); doi: 10.1117/12.323596
Show Author Affiliations
HePing Pan, Cooperative Research Ctr. for Sensor Signal and Information Processing (China)
Nickens N. Okello, Cooperative Research Ctr. for Sensor Signal and Information Processing (United States)
Daniel W. McMichael, Cooperative Research Ctr. for Sensor Signal and Information Processing (Australia)
Matthew Roughan, Cooperative Research Ctr. for Sensor Signal and Information Processing (United States)


Published in SPIE Proceedings Vol. 3545:
International Symposium on Multispectral Image Processing (ISMIP'98)
Ji Zhou; Anil K. Jain; Tianxu Zhang; Yaoting Zhu; Mingyue Ding; Jianguo Liu, Editor(s)

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