A Multiplier Method for Solving the Distributed Binary Hypothesis Testing Problem

This paper considers a distributed binary hypothesis testing problem in which a number of subordinate decision makers (DMs, sensors) transmit thir opinions, based on their own data, to a primary decision maker who combines these opinions and makes the final team decision. The primary decision maker does not receive any measurments (of his own) from the environment and only acts as a fusion center. It is well known [5] that the decision rules of individual DMs are in the form of likelihood ratio tests, that is, the subordinate DMs are constrained to operate on their Receiver Operating Characteristic (OC) curves. In this paper, we show that the data fusion problem is equivalent to a nondifferentiable optimization problem, and solve it using the multiplier method.