Non-cooperative Distributed Detection via Federated Sensor Networks

In this study, we address the challenge of noncooperative target detection by federating two wireless sensor networks. The objective is to capitalize on the diversity achievable from both sensing and reporting phases. The target's presence results in an unknown signal that is influenced by unknown distances between the sensors and target, as well as by symmetrical and single-peaked noise. The fusion center, responsible for making more accurate decisions, receives quantized sensor observations through error-prone binary symmetric channels. This leads to a two-sided testing problem with nuisance parameters (the target position) only present under the alternative hypothesis. To tackle this challenge, we present a generalized likelihood ratio test and design a fusion rule based on a generalized Rao test to reduce the computational complexity. Our results demonstrate the efficacy of the Rao test in terms of detection/false-alarm rate and computational simplicity, highlighting the advantage of designing the system using federation.