Abstract

In this article, we study the distributed state estimation problem for a network of interconnected subsystems with cross-correlated noises. For the linear dynamic system, we derive a new distributed state estimation algorithm based on Kalman filtering and the newly constructed measurements whose measurement noises are uncorrelated. The proposed algorithm applies to acyclic graphs with fast finite-time convergence. Moreover, if the process and measurement noises of all subsystems are mutually uncorrelated, the distributed state estimator and predictor of this paper are reduced to a known method in literature. Finally, an example is used to demonstrate the effectiveness of the proposed algorithm.

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