Distributed sensor layout optimization for target detection with data fusion

Distributed detection with data fusion has gained great attention in recent years. Collaborative detection improves the performance, and the optimal sensor deployment may change with time. It has been shown that with data fusion less sensors are needed to get the same detection ability when abundant sensors are deployed randomly. However, because of limitations on equipment number and deployment methods, fixed sensor locations may be preferred underwater. In this paper, we try to establish a theoretical framework for finding sensor positions to maximize the detection probability with a distributed sensor network. With joint data processing, detection performance is related to all the sensor locations; as sensor number grows, the optimization problem would become more difficult. To simplify the demonstration, we choose a 1-dimensional line deployment model and present the relevant numerical results.