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Accuracy analysis for distributed dynamic state estimation in large-scale systems with a cyclic network graph
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DOI：10.1007/s11432-022-3846-1.png)
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Abstract
En 中文
This paper addresses a distributed dynamic state estimation problem in large-scale systems characterized by a cyclic network graph. The objective is to develop a distributed estimation algorithm for each node to generate local state estimations, based on the coupled measurements and boundary information exchanged with neighboring nodes. Our proposed approach is grounded in the maximum a posteriori (MAP) estimation method, which yields suboptimal results in acyclic network graphs compared with the centralized MAP approach. We extend this approach to systems with a cyclic network graph. Furthermore, we provide an accuracy analysis by deriving bounds for the differences in estimation error covariance and state estimation between the proposed distributed algorithm and the suboptimal centralized MAP method. These bounds apply to a specific category of systems that satisfy certain conditions, including cyclic topology and sparse connections. We demonstrate that these bounds converge asymptotically, with the rate of convergence determined by the loop-free depth of the graph. The loop-free depth of the graph refers to the maximum number of nodes that can be traversed in a cycle without revisiting any node. Finally, we demonstrate the validity of the algorithm through numerical examples.
Keywords:
distributed state estimation
maximum a posteriori estimation
cyclic network graph
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