State fusion with unknown correlation: Ellipsoidal intersection

Some crucial challenges of estimation over sensor networks are reaching consensus on the estimates of different systems in the network and separating the mutual information of two estimates from their exclusive information. Current fusion methods of two estimates tend to bypass the mutual information and directly optimize the fused estimate. Moreover, both the mean and covariance of the fused estimate are fully determined by optimizing the covariance only. In contrast to that, this paper proposes a novel fusion method in which the mutual information results in an additional estimate, which defines a mutual mean and covariance. Both variables are derived from the two initial estimates. The mutual covariance is used to optimize the fused covariance, while the mutual mean optimizes the fused mean. An example of decentralized state estimation, where the proposed fusion method is applied, shows a reduction in estimation error compared to the existing alternatives.

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