Theoretical Bounds in Minimax Decentralized Hypothesis Testing
Gökhan Gül, Abdelhak M. Zoubir · IEEE Transactions on Signal Processing · 2016
Minimax decentralized detection is studied under two scenarios: with and without a fusion center when the source of uncertainty is the Bayesian prior. When there is no fusion center, the constraints in the network design are determined. Both for a single decision maker and multiple decision makers, the maximum loss in detection performance due to minimax decision making is obtained. In the presence of a fusion center, the maximum loss of detection performance between with and without fusion center networks is derived assuming that both networks are minimax robust. The results are finally generalized.