Data-Driven Distributed Information-Weighted Consensus Filtering in Discrete-Time Sensor Networks With Switching Topologies

This article proposes a data-driven distributed filtering method based on the consensus protocol and information-weighted strategy for discrete-time sensor networks with switching topologies. By introducing a data-driven method, a linear-like state equation is designed by utilizing only the input an...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 53; H. 12; S. 7548 - 7559
Hauptverfasser: Ji, Honghai, Wei, Yuzhou, Fan, Lingling, Liu, Shida, Hou, Zhongsheng, Wang, Li
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Data-driven identification
distributed state estimation
Equations of state
Estimation
Filtration
Kalman filters
Mathematical models
Network topologies
Network topology
nonlinear filtering
Optimization
Parameter identification
sensor networks
State estimation
Switches
Switching
switching topologies
Topology
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:This article proposes a data-driven distributed filtering method based on the consensus protocol and information-weighted strategy for discrete-time sensor networks with switching topologies. By introducing a data-driven method, a linear-like state equation is designed by utilizing only the input and output (I/O) data without a controlled object model. In the identification step, data-driven adaptive optimization recursive identification (DD-AORI) is exploited to identify the recurrence of time-varying parameters. It is proved that for discrete-time switching networks, estimation errors of all nodes are ultimately bounded when data-driven distributed information-weighted consensus filtering (DD-DICF) is executed. The algorithm combines with the received neighbors and direct or indirect observations for the target node to produce modified gains, resulting in a novel state estimator containing an information interaction mechanism. Subsequently, convergence analysis is performed on the basis of the Lyapunov equation to guarantee the boundedness of DD-DICF estimate error. Simulations verify the performance of the DD-DICF against the theoretical results as well as in comparison with some existing filtering algorithms.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3166649