Decentralized Bayesian detection with feedback

A decentralized detection system with feedback and memory using the Bayesian formulation is investigated. The optimization of this system results in a likelihood ratio test at the local detectors for statistically independent observations. In addition, local detector thresholds and the system probability of error are shown to be a function of the fed back global decision. The issue of data transmission between local detectors and the fusion center is addressed. Two protocols are proposed and studied to reduce data transmissions. Numerical examples are also presented for illustration.