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A Bayesian sampling approach to decision fusion using hierarchical models

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2 Author(s)
Biao Chen ; Dept. of Electr. Eng. & Comput. Sci., Syracuse Univ., NY, USA ; Varshney, P.K.

Data fusion and distributed detection have been studied extensively, and numerous results have been obtained in the literature. In this paper, the design of a fusion rule for distributed detection problems is re-examined, and a novel approach using Bayesian inference tools is proposed. Specifically, the decision fusion problem is reformulated using hierarchical models, and a Gibbs sampler is proposed to perform posterior probability-based fusion. Performance-wise, it is essentially identical to the optimal likelihood-based fusion rule whenever it exists. The true merit of this approach is its applicability to various complex situations, e.g., in dealing with unknown signal/noise statistics where the likelihood-based fusion rule may not be easy to obtain or may not even exist

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Signal Processing, IEEE Transactions on  (Volume:50 ,  Issue: 8 )