Fuzzy causal probabilistic networks and multisensor data fusion
Heping Pan, Nickens N. Okello, Daniel W. McMichael, Matthew Roughan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
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.