Adaptive Detection in Distributed Networks Using Maximum Likelihood Detector

This letter describes a maximum likelihood distributed detector algorithm with a special initialization that yields an exponential decay rate on error probabilities in transient, using the adaptive diffusion least mean squares (LMS) algorithm in a distributed sensor network. The nodes that compose the network must decide between two concurrent hypotheses concerning the environment where they are inserted, using local measurements and shared estimates coming from their neighbors. It is shown analytically and by simulations that the exponential performance does not depend on the LMS step size, provided it is sufficiently small.