Fuzzy Causal Probabilistic Networks and Multisensor Data Fusion

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 fuzzy causal probabilistic network (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 (CPN), also called Bayesian networks, belief networks, or innuence diagrams, by associating each discrete variable with a fuzziier and a defuzziier, if required. A fuzziier converts a crisp variable to a fuzzy discrete variable while a defuzziier 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 eld 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.