Simple counting rule for optimal data fusion

In the problem of optimal decision fusion, the data fusion center receives the information sent independently by each detector. Z. Chair and P.K. Varshney et al., (1986) showed that, the optimal decision rule is a weighted sum of local decisions, and the weight is a function of the probability of detection (P_D) and the probability of false alarm (P_F).P_D and P_F for each detector must be known, but this information is not always available practically and these probabilities may not be constant with the time. In this paper, we have presented an adaptive fusion model which estimate the P_D and P_F adaptively by a simple counting. Reference signals are not given, so the fused decision of all detectors is considered as the reference signal, the decision of a local detector is arbitrated by this fusion result.