On the Underwater Target Detection with Decision Fusion in UASN

Abstract

Data fusion in multi-sensor networks can significantly improve the perception gain of targets. We re-investigated the detection fusion system by considering the local decision rule and fusion rule as a whole. Based on the characteristics of underwater target detection in underwater acoustic sensor network (UASN), we construct a fusion detection system with parallel topology. In such a system, the energy detector is employed in each sensor while the Chair-Varshney and Counting decision fusion strategies are taken into account, respectively. In addition, the decision statistics in each sensor and the fusion statistics in the fusion center are used to determine the detection threshold in each sensor and the fusion center by Monte Carlo simulation. The results show that the performance of fusion system performs much better than single sensor detection system. When the detection distance is larger, Chair-Varshney fusion statistics and Counting fusion statistics have the comparable detection performance.