In this correspondence, we present a technique to find the optimal threshold for the binary hypothesis detection problem with identical and independent sensors. The sensors all use an identical and single threshold to make local decisions, and the fusion center makes a global decision based on the local binary decisions. For generalized Gaussian noises and some non-Gaussian noise distributions, we show that for any admissible fusion rule, the probability of error is a quasi-convex function of threshold . Hence, the problem decomposes into a series of quasi-convex optimization problems that may be solved using well-known techniques. Assuming equal a priori probability, we give a sufficient condition of the non-Gaussian noise distribution ( ) for the probability of error to be quasi-convex. Furthermore, this technique is extended to Bayes risk and Neyman--Pearson criteria. We also demonstrate that, in practice, it takes fewer than twice as many binary sensors to give the performance of infinite precision sensors in our scenario.

ii DEDICATION To my parents, who love me, To Wei, who loves and supports me, To Frank, who likes smiling

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