Adaptive fusion model for distributed detection system

There has been a growing interest in developing efficient and reliable distributed detection systems for target recognition and communications. Chair and Varshney have derived an optimal decision rule for fusing decisions based on the Bayesian criterion. To implement the rule, the probability of detection P_D and the probability of false alarm P_F for each detector must be known, but this information is not always available in practice. This paper presents an adaptive fusion model which estimate the P_D and P_F adaptively during the decision fusing process. The estimation is implemented by a simple statistical method. That is, the estimates for P_D and P_F of the ith detector are obtained by counting the number of its decisions that are considered to be correct and incorrect, respectively. Since reference signals are not given, whether the decision of a local detector is considered correct or incorrect is arbitrated by the fused decision of all the other local detectors; that is, the fused decision of all other local detectors is used as the reference for the ith detector. Furthermore, in the work, the fused results of the other local decisions are classified as 'reliable' and 'unreliable'. Only reliable decisions are used to develop the decision rule. Analysis on classifying the fused decisions in terms of reducing estimation error is given, and simulation results which conform to our analysis are presented.