Fuzzy causal probabilistic networks and multisensor: Data fusion

被引：1

作者：

Pan, HP ^{[1
]}

Okello, N ^{[1
]}

McMichael, D ^{[1
]}

Roughan, M ^{[1
]}

机构：

[1] Cooperat Res Ctr Sensor Signal & Informat Proc, The Levels, SA 5095, Australia

来源：

INTERNATIONAL SYMPOSIUM ON MULTISPECTRAL IMAGE PROCESSING | 1998年 / 3545卷

关键词：

multisource information fusion; multisensor data fusion; fuzzy causal probabilistic networks; airborne early warning and control; target tracking using radars;

D O I：

10.1117/12.323596

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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 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 held 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.

引用

页码：550 / 561

页数：12