Exploiting Optimal Threshold for Decision Fusion in Wireless Sensor Networks

Decision fusion has been adopted in a number of sensor systems to deal with sensing uncertainty and enable the sensors to collaborate with each other. It can distribute computation workload and significantly reduces the communication overhead. However, some variants of decision rules such as Voting, Bayes Criterion, and Neyman-Pearson require a priori knowledge on the probability of targets presence which is still an open issue in detection theory. In this paper, we propose a binary decision fusion scheme that reaches a global decision by integrating local decisions made by fusion members. The optimal local thresholds and global threshold are derived by using the Minimax criterion based analysis while they are ensuring false alarm rate constraint, without a preestimated target appearance probability. Simulation results show that our scheme can improve the system performance under certain constraints, which can guide the threshold selection for implementing WSN systems in mission-critical applications.