Learning and Exploiting Partial Knowledge in Distributed Estimation

In distributed estimation, several sensor nodes provide estimates of the same underlying dynamic process. These estimates are correlated but due to local processing, the correlations are only partially known or even unknown. For a consistent fusion of the local estimates, the correlation needs to be properly treated. Many methods provide consistent but overly conservative fusion results. In this paper, we propose to learn partial knowledge about the correlation in the form of correlation sets and exploit this knowledge to provide less conservative estimates. We use a simple numerical example to demonstrate the advantages of the proposed approach in terms of quality and consistency and how the quality of the fused estimate increases with time.