Distributed Evidential EM Algorithm for Gaussian Mixtures in Sensor Network with Uncertain Data

In this paper, the problem of clustering in distributed sensor networks with uncertain measurements is considered. It is assumed that each node in the sensor network can be described as a mixture of some elementary conditions. Therefore, the measurements of the sensors can be modeled using a Gaussian mixture model, in which the uncertainty on the attributes is represented by the belief functions. We present a novel algorithm, called distributed evidential expectation maximization (DEEM) algorithm, for the estimation of the Gaussian components in the mixture model. The effectiveness of the proposed algorithm is demonstrated through simulations of sensor networks with uncertain data.