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Data-Driven Distributed Information-Weighted Consensus Filtering in Discrete-Time Sensor Networks With Switching Topologies
- Source :
- IEEE Transactions on Cybernetics; December 2023, Vol. 53 Issue: 12 p7548-7559, 12p
- Publication Year :
- 2023
-
Abstract
- 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.
Details
- Language :
- English
- ISSN :
- 21682267
- Volume :
- 53
- Issue :
- 12
- Database :
- Supplemental Index
- Journal :
- IEEE Transactions on Cybernetics
- Publication Type :
- Periodical
- Accession number :
- ejs64723379
- Full Text :
- https://doi.org/10.1109/TCYB.2022.3166649