Blind Adaptive Algorithm for M-Ary Distributed Detection

In a parallel distributed detection system each local detector makes a decision based on its own observation, then transmits its local decision to a fusion center. Given fixed local decision rules, in order to design the optimal fusion rule for the M hypotheses, the fusion center needs to have perfect knowledge of the performance of the local detectors as well as the prior probability of the hypotheses. Such knowledge may not be available in practice. In this paper, we propose a suboptimal algorithm for M-ary decision fusion based on binary groupings of multiple hypotheses. Simulation results show that this method is effective in practice.