Decision fusion for parallel sequential sensors

Lee and Thomas (1984) have introduced a modified version of Wald's sequential probability ratio test. The modified version retains most of the features of Wald's procedure but is easier to analyze and offers efficient truncation procedures. In this study, we use the Lee-Thomas design to analyze the performance of a bank of M parallel sequential sensors whose decisions are fused. We evaluate the performance of the sensor bank by two criteria: (1) the probability of error; (2) average sample number (ASN) needed to achieve it. Three rules are studied: (1) first-to-decide rule (Niu and Varshney, 1984): once at least one sensor has stopped sampling, we adopt the decision of one of the stopped sensors; (2) all-that-decided rule: once at least one sensor has stopped sampling, we integrate all the decisions of stopped sensors through the 1986 Chair-Varshney decision fusion rule; and (3) all-sensors rule: once at least one sensor has stopped sampling, we combine the available decisions of the stopped sensor and the implied decisions of the remaining sensors. Performance of the three rules is calculated and gains with respect to the performance of a single sensor are quantified.