Distributed detection fusion via Monte Carlo importance sampling

@article{Rao2016DistributedDF,
  title={Distributed detection fusion via Monte Carlo importance sampling},
  author={Hang Rao and Xiaojing Shen and Yunmin Zhu and Jianxin Pan},
  journal={2016 35th Chinese Control Conference (CCC)},
  year={2016},
  pages={4830-4835}
}
Distributed detection fusion with high-dimension conditionally dependent observations is known to be a challenging problem. When a fusion rule is fixed, this paper attempts to make progress on this problem for the large sensor networks by proposing a new Monte Carlo framework. Through the Monte Carlo importance sampling, we derive a necessary condition for optimal sensor decision rules in the sense of minimizing the approximated Bayesian cost function. Then, a Gauss-Seidel/person-by-person… 

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