Use of Bayesian belief networks to fuse continuous and discrete information for target recognition, tracking, and situation assessment
Leland T. Stewart, Perry McCarty · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
This paper describes the use of Bayesian belief networks for the fusion of continuous and discrete information. Bayesian belief networks provide a convenient and straightforward way of modeling the relationships between uncertain quantities. They also provide efficient computational algorithms. Most current applications of belief networks are restricted to either discrete or continuous quantities. We present a methodology that allows both discrete and continuous variables in the same network. This extension makes possible the fusion of information from, or inferences about, such diverse quantities as sensor output, target location, target type or ID, intent, operator judgment, behavior profile, etc.