Distributed detection fusion with nonideal channels under Monte Carlo framework

The distributed detection fusion is investigated for conditionally dependent sensor networks with channel errors. When the joint probability density functions of the sensor observations are dependent and high dimensional, it is known to be a challenging problem. This paper deals with this problem under Monte Carlo framework. The Bayesian cost function is approximated by Monte Carlo importance sampling. Necessary conditions for optimal sensor rules and optimal fusion rule are derived in the sense of minimizing the approximated Bayesian cost function, respectively. A Gauss-Seidel/person-by-person optimization algorithm is developed to search the optimal sensor rules. It is proved that the discretized algorithm is finitely convergent. Since the error rate of Monte Carlo integration is regardless of dimensionality, the complexity of the new algorithm is much less than that of the previous algorithm based on Riemann sum approximation. The proposed method allows us to design the sensor networks with a higher dimensional joint probability density function of the sensor observations. The typical examples with dependent observations and channel errors are examined. The results of numerical examples demonstrate the effectiveness of the new algorithm.