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Partial-Nodes-Based Estimation for Complex Networks With Random Inner Coupling
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DOI：10.1109/TSMC.2025.3626136.png)
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Abstract
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This article investigates distributed state estimation of complex networks (CNs) with limited communication capacity. A random transmission strategy is used to overcome the communication capacity constraint between two nodes. A distributed state estimator that makes use of the partially available measurements is designed. To handle the cross-term, Young’s inequality is employed, and an upper bound (UB) for the state prediction error covariance (PEC) is derived. An optimal estimation gain is then devised based on the derived state PEC. The stability of the UB is analyzed using a vectorization approach, and then a sufficient condition for stability is obtained. Finally, a numerical simulation is carried out to validate the effectiveness of the proposed distributed estimator and confirm the accuracy of the derived UB.
Keywords:
Complex networks (CNs)
distributed state estimation
measurements of partial nodes
random inner coupling
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