Quantization of Prior Probabilities for Collaborative Distributed Hypothesis Testing

This paper studies the quantization of prior probabilities, drawn from an ensemble, in distributed detection with data fusion by combination of binary local decisions. Design and performance equivalences between a team of
N
agents and a more powerful single agent are obtained. Effects of identical quantization and diverse quantization on mean Bayes risk are compared. It is shown that when agents using diverse quantizers interact to agree on a perceived common risk, the effective number quantization levels is increased. With this collaboration, optimal diverse regular quantization with
K
cells per quantizer performs as well as optimal identical quantization with
N
(
K
-1)+1 cells per quantizer. Similar results are obtained for the maximum Bayes risk error criterion.