In this paper we study a dynamic sensor selection method for Bayesian filtering problems. In particular we consider the distributed Bayesian Filtering strategy given in [1] and show that the principle of mutual information maximization follows naturally from the expected un-certainty minimization criterion in a Bayesian filtering framework. This equivalence results in a computationally feasible approach to state estimation in sensor networks. We illustrate the application of the proposed dynamic sensor selection method to both discrete and linear Gaussian models for distributed tracking as well as to stationary target localization using acoustic arrays.
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