Global optimization for distributed detection system under the constraint of likelihood ratio quantizers

This paper considers the problem of distributed Bayesian detection. The detection system is consisting of a fusion center and N local sensors, and each sensor quantizer is allowed to produce multi-bit sensor output. To optimize the system performance, the global optimization of the fusion rule and of the sensor quantizer mappings is needed. Usually, an optimal solution to the global optimization problem can be obtained only for conditionally independent sensors. As for dependent sensors, although the necessary conditions for global optimization can be found, an optimal solution usually can not be obtained. Thus, for distributed detection systems consisting of dependent sensors, some suboptimal global optimization method need to be considered. In this paper, we consider this suboptimal global optimization problem for distributed and quantized Bayesian detection systems. We constrain the sensor quantizers to be likelihood ratio quantizers, and optimize the system performance under this constraint.