OPTIMAL DISTRIBUTED DETECTION OF MULTIPLE HYPOTHESES USING BLIND ALGORITHM

In a parallel distributed detection in order to design the optimal fusion rule, the fusion center needs to have perfect knowledge of the performance of the local detectors as well as the prior probabilities of the hypotheses. Such knowledge is not available in most practical cases. In this paper, we propose a blind technique for the $M$-ary distributed detection problem. We derive the probability mass function of the local decisions and use this result to develop maximum likelihood estimates of unknown parameters. We also derive analytically the overall detection performance for both binary and $M$-ary distributed detection and discuss the difference of the overall detection performance obtained using the estimated values of unknown parameters and their true values. Finally, we demonstrate the applicability of our results through numerical examples.