Simultaneous distributed estimation and classification in sensor networks

In this work we consider the problem of simultaneously classifying sensor types and estimating hidden parameters in a network of sensors subject to gossip-like communication limitations. In particular, we consider a network of scalar noisy sensors which measure a common unknown parameter. We assume that a fraction of the nodes is subject to the same (but possibly unknown) offset. The goal for each node is to simultaneously identify the class the node belongs to and to estimate the common unknown parameter, only through local communication and computation. We propose a distributed estimator based on the maximum likelihood (ML) approach and we show that, in case the offset is known, this estimator converges to the centralized ML as the number N of sensor nodes goes to infinity. We also compare this strategy with a distributed implementation of estimation-maximization (EM) algorithm; we show tradeoffs via numerical simulations in terms of robustness, speed of convergence and implementation simplicity.