Optimization of Distributed Detection Systems under Neyman-Pearson Criterion

Abstract

In this paper, the problem of distributed detection under Neyman-Pearson criterion is considered. We assume that the observations of different sensors are conditionally dependent. First, an important property of the overall ROCs is investigated. Based on this property, necessary conditions for optimal fusion rule and sensor decision rules are then obtained. In the derivation of our optimality conditions, no assumption regarding the convexity of the overall ROC is assumed. Instead, we assume the differentiability of the overall ROCs. The method used here is straightforward, and the result obtained is clear and simple. Some relations between our results and the Lagrange method exist, and the implication of our results to the validity of Lagrange method is also investigated