CONSENSUS-LIKE ALGORITHMS FOR ESTIMATION OF GAUSSIAN MIXTURES OVER LARGE SCALE NETWORKS

In this paper, we address the problem of estimating Gaussian mixtures in a sensor network. The scenario we consider is the following: a common signal is acquired by sensors, whose measurements are affected by standard Gaussian noise and by different offsets. The measurements can thus be statistically modeled as mixtures of Gaussians with equal variance and different expected values. The aim of the network is to achieve a common estimation of the signal, and to cluster the sensors according to their own offsets.