Fuzzy causal probabilistic networks and multisensor data fusion

HePing Pan; Nickens N. Okello; Daniel W. McMichael; Matthew Roughan

doi:10.1117/12.323596

25 September 1998 Fuzzy causal probabilistic networks and multisensor data fusion

HePing Pan, Nickens N. Okello, Daniel W. McMichael, Matthew Roughan

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.

Citation Download Citation

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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