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Bayesian inference network: applications to target tracking

Data fusion concepts have been applied in many disciplines, but a general systematic formulation has not been well developed. This paper is intended to provide a guideline in applying data fusion techniques to a practical problem, the fusion of target identification (ID) attributes measurements. Formation of a consensus function is first presented, and is followed by construction of a hierarchical probabilistic network for computing a joint probability density. An ID fusion processing approach is described and integrated into a generalized track/data association algorithm.