Optimal Distributed Decision Fusion with Fuzzy a priori Probabilities and Fuzzy Cost Functions Based on Minimum Bayesian Risk Criterion
Wang Guo-hon · Dianzi xuebao · 1999
When the a priori prohabilities and imt functions are fuzzy, the optimal decision fusion in the sense 0f minimum Bayesian risk at the fusion center is considered. The fusion center receives decisions from various distributed sensors andfour optimal decision fusion schemes at the fusion center are derived. It is discovered that the optimal decision fusion rule is aweighted sum of local decisions in this case, the weights are functions of the prohability of detection and the probaility of falsealarm of the detector, and that the threshold depends not noly on the fuzzy a priori probabilities and cost functions but also onthe criterion used for defuzzfying fuzzy sets. Through the simulation, an optimal decision fusion scheme which is most suitablefor fuzzy a priori probabilities and cost functions with trapezoidal membership functions is found.