Distributed Filtering under Gossip Strategy over Sensor Networks with Fading Measurements

This paper aims to discusses the distributed filtering problem under the gossip communication protocol under the consideration of fading phenomenon. In order to efficiently transmit, merge local data and save bandwidth, a gossip communication protocol is introduced, drawing inspiration from the social phenomenon of gossiping. In addition, the Rayleigh fading measurement is taken into account in the channel between the sensor and the filter. By applying the principles of Lyapunov stability theory, a set of matrix inequalities is established to guarantee the system’s boundedness in terms of mean square sense. A numerical example is given to demonstrate the effectiveness of the proposed results.

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Publication Details

Published
2025-06-20
DOI
https://doi.org/10.1109/icecai66283.2025.11171034
Primary Topic
Distributed Sensor Networks and Detection Algorithms
Type
article
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article

Distributed Filtering under Gossip Strategy over Sensor Networks with Fading Measurements

Wenbo Dong, Xuerong Li, Yan Xu, Fan Yang
Distributed Sensor Networks and Detection Algorithms
article

Distributed Filtering under Gossip Strategy over Sensor Networks with Fading Measurements

Wenbo Dong, Xuerong Li, Yan Xu, Fan Yang
article en

Abstract

This paper aims to discusses the distributed filtering problem under the gossip communication protocol under the consideration of fading phenomenon. In order to efficiently transmit, merge local data and save bandwidth, a gossip communication protocol is introduced, drawing inspiration from the social phenomenon of gossiping. In addition, the Rayleigh fading measurement is taken into account in the channel between the sensor and the filter. By applying the principles of Lyapunov stability theory, a set of matrix inequalities is established to guarantee the system’s boundedness in terms of mean square sense. A numerical example is given to demonstrate the effectiveness of the proposed results.

Northeast Petroleum University (CN)
Openalex Percentile: Top 69%
Distributed Sensor Networks and Detection Algorithms
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