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25 September 1998 Fuzzy causal probabilistic networks and multisensor data fusion
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Proceedings Volume 3545, International Symposium on Multispectral Image Processing (ISMIP'98); (1998) https://doi.org/10.1117/12.323596
Event: International Symposium on Multispectral Image Processing, 1998, Wuhan, China
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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
HePing Pan, Nickens N. Okello, Daniel W. McMichael, and Matthew Roughan "Fuzzy causal probabilistic networks and multisensor data fusion", Proc. SPIE 3545, International Symposium on Multispectral Image Processing (ISMIP'98), (25 September 1998); https://doi.org/10.1117/12.323596
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