Hierarchical Fusion Estimation With Feedback for Clustered Sensor Networks Subject to Leader and Subordinate Sensors

In this brief, hierarchical fusion estimation with feedback is researched in clustered sensor networks with leader and subordinate sensors. A local estimator is designed to obtain local estimates using feedback fusion estimates in each sensor. A two-layer fusion estimator is developed to achieve fusion estimates for improving estimation accuracy in each leader sensor. The first layer fusion estimator is proposed combining local estimates within the same cluster based on both current and past local estimation accuracy under inaccurate noise covariance matrices. The second layer fusion estimator is designed by fusing the first layer fusion estimates from all leader sensors. Validity of the hierarchical fusion estimation with feedback is shown based on a maneuvering target tracking system.

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Publication Details

Journal
IEEE Transactions on Circuits & Systems II Express Briefs
Published
2025-08-12
DOI
https://doi.org/10.1109/tcsii.2025.3598216
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Hierarchical Fusion Estimation With Feedback for Clustered Sensor Networks Subject to Leader and Subordinate Sensors

Ling Zhao, Yuanqing Xia, Hongjiu Yang, Xinyue Cao
IEEE Transactions on Circuits & Systems II Express Briefs
Target Tracking and Data Fusion in Sensor Networks
article

Hierarchical Fusion Estimation With Feedback for Clustered Sensor Networks Subject to Leader and Subordinate Sensors

Ling Zhao, Yuanqing Xia, Hongjiu Yang, Xinyue Cao
article en

Abstract

In this brief, hierarchical fusion estimation with feedback is researched in clustered sensor networks with leader and subordinate sensors. A local estimator is designed to obtain local estimates using feedback fusion estimates in each sensor. A two-layer fusion estimator is developed to achieve fusion estimates for improving estimation accuracy in each leader sensor. The first layer fusion estimator is proposed combining local estimates within the same cluster based on both current and past local estimation accuracy under inaccurate noise covariance matrices. The second layer fusion estimator is designed by fusing the first layer fusion estimates from all leader sensors. Validity of the hierarchical fusion estimation with feedback is shown based on a maneuvering target tracking system.

IEEE Transactions on Circuits & Systems II Express BriefsVol. 72(10)
Beijing Institute of Technology (CN), Tianjin University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 89%
Target Tracking and Data Fusion in Sensor Networks
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