Covariance consistency methods for fault-tolerant distributed data fusion

Abstract This paper presents a general, rigorous, and fault-tolerant framework for maintaining consistent mean and covariance estimates in an arbitrary, dynamic, distributed network of information processing nodes. In particular, a solution is provided that addresses the information deconfliction problem that arises when estimates from two or more different nodes are determined to be inconsistent with each other, e.g., when two high precision (small covariance) estimates place the position of a particular object at very different locations. The challenge is to be able to resolve such inconsistencies without having to access and exploit global information to determine which of the estimates is spurious. The solution proposed in this paper is called Covariance Union.

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