Adaptive Decision Fusion by Simple Reinforcement Learning

In the problem of optimal fusing decisions, the probability of detection (PD) and the probability of false alarm (PF) for each detector must be known, but this information is not always available practically. In this paper we presented an adaptive fusion model which estimates the PDand PFadaptively 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