A Bayesian sampling approach to decision fusion using hierarchical models
Biao Chen, Pramod K. Varshney · IEEE Transactions on Signal Processing · 2002
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.