Saddlepoint Approximation for Sensor Network Optimization

The task of detection optimization in sensor networks is hindered by the large computational cost of evaluating the performance criteria, e.g. the probability of making wrong decisions. We present an approach that avoids these obstacles by considering a rather accurate approximation to computing the detection performance. We propose the saddlepoint approximation and provide results that demonstrate its high accuracy and low complexity. The results are used to show that, for a range of problems, the optimal fusion rule is equivalent to a simple majority rule.