Bayes Networks for Diverse-State and Large-Scale Fusion
نویسنده
چکیده
Generalized inference provides an elegant formulation for fusing sources that have many diverse states that are nonetheless inter-related, be it in often in weak and complex ways. Indeed, levels 1 through 3 fusion can be characterized as inferring states from evidence; estimation can be viewed as a specific inference discipline. Unfortunately, the elegant inference formulation rapidly becomes intractably complex for any real-world problems due to the permutations of interrelationships between the interacting state variables. Bayesian networks provide a way of coping with the complexity. Bayesian networks are techniques for making probabilistic inference tractable and have been in broad literature and research for quite some time. This paper describes the application of the Bayes network technique to a real-world large-scale fusion problem. It provides experience with the many adaptations and extensions that are required and illustrates some issues that need further research.
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تاریخ انتشار 2007