Distributed detection over adaptive networks based on diffusion estimation schemes

We study the problem of distributed detection, where a set of nodes are required to decide between two hypotheses based on their measurements. We seek fully distributed implementations, where all nodes make individual decisions by communicating with their immediate neighbors, and no fusion center is necessary. This scheme provides the network with more flexibility, saves energy for communication and networking resources. Our distributed detection algorithm is based on a previously proposed distributed estimation algorithm. We establish the connection between the detection and estimation problems, propose a distributed detection algorithm, and analyze the performance of the algorithm in terms of its probabilities of detection and false alarm. We also provide simulation results comparing with other cooperation schemes.