A Bayesian sampling approach to decision fusion using hierarchical models
- 7 August 2002
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Signal Processing
- Vol. 50 (8) , 1809-1818
- https://doi.org/10.1109/tsp.2002.800419
Abstract
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.Keywords
This publication has 14 references indexed in Scilit:
- Distributed signal detection under the Neyman-Pearson criterionIEEE Transactions on Information Theory, 2001
- The good, bad and ugly: distributed detection of a known signal in dependent Gaussian noiseIEEE Transactions on Signal Processing, 2000
- The Selection of Prior Distributions by Formal RulesJournal of the American Statistical Association, 1996
- The case for like-sensor predetection fusionIEEE Transactions on Aerospace and Electronic Systems, 1994
- Optimal data fusion of correlated local decisions in multiple sensor detection systemsIEEE Transactions on Aerospace and Electronic Systems, 1992
- Optimum multisensor fusion of correlated local decisionsIEEE Transactions on Aerospace and Electronic Systems, 1991
- Hardware complexity of binary distributed detection systems with isolated local Bayesian detectorsIEEE Transactions on Systems, Man, and Cybernetics, 1991
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- Non-Uniform Random Variate GenerationPublished by Springer Nature ,1986
- Optimal Data Fusion in Multiple Sensor Detection SystemsIEEE Transactions on Aerospace and Electronic Systems, 1986