A two-level distributed decision system consisting of a number of local decision makers (LDM's) connected to a global decision maker (GDM) is considered. The LDM's share a common M-hypothesis testing problem, have their own observations independent of each other, and employ likelihood ratios for their decision-making. Each LDM transmits its inference to the GDM where the final decision is derived. The local inferences consist of the ranking of the candidate hypotheses and a degree of confidence based on likelihood ratios. Using a maximum distance criterion, the optimum confidence-based subpartitioning of local decision space is studied. It is shown that the presented system could greatly outperform the one where each LDM provides a single hypothesis hard decision and perform nearly as well as the optimum centralized system.
ASJC Scopus subject areas
- Computer Science Applications
- Control and Systems Engineering
- Electrical and Electronic Engineering