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Distributed Innovation-Constrained State Estimation Over Sensor Networks: A State-Dependent Coefficient Parameterization Approach
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AI
DOI：10.1109/tac.2026.3680120.png)
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摘要
En 中文
This article focuses on the distributed state estimation problem for a specific type of nonlinear systems over sensor networks. The state estimation is accomplished on each sensor node by utilizing local measurements along with network-shared innovations from neighboring nodes. A state-dependent coefficient parameterization technique is employed to express the original nonlinear system in a pseudolinear form. In addition, a saturation-like scheme is used to limit the impact of anomalous signals during propagation and ensure the received information remains within a permissible range. The main goal of the research problem is to propose an estimation approach that restricts the estimation errors within predefined ellipsoids. Adequate conditions for solving this problem are provided by establishing the feasibility of a series of matrix inequalities. Further, a technique is developed to determine the locally optimal estimator parameters based on the established framework. Finally, the correctness of the results is demonstrated using a simulation example that involves the Van der Pol equation.
Keyword:
Distributed state estimation
innovation constraints
set-membership estimation
state-dependent coefficient parameterization (SDCP)
wireless sensor networks (WSNs)
期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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引用论文
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AUTOMATICA
OAAI
AUTOMATICAIF5.9
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