Enhanced Distributed Outlier-Resilient Fusion Estimation With Novel Dimensionality Reduction Under IT-2 T-S Fuzzy System

This article addresses an enhanced distributed outlier-resilient fusion estimation problem using an interval type-2 (IT-2) Takagi-Sugeno (T-S) fuzzy model, integrating outlier detection schemes and dimensionality reduction (DR) strategies. First, the IT-2 T-S fuzzy model is employed to handle system...

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Published inIEEE transactions on fuzzy systems Vol. 32; no. 11; pp. 6044 - 6055
Main Authors Yang, Yunyi, Wen, Guoguang, Wang, Yidi, Peng, Zhaoxia, Xiong, Kai
Format Journal Article
LanguageEnglish
Published IEEE 01.11.2024
Subjects
Anomaly detection
Dimensionality reduction
Dimensionality reduction (DR)
distributed fusion estimation
Estimation
Fuzzy systems
interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy system
Noise measurement
outlier detection
Protocols
Robot sensing systems
Round–Robin protocol (RRP)
Takagi-Sugeno model
zonotopic set-membership filter
Online AccessGet full text
ISSN1063-6706
1941-0034
DOI10.1109/TFUZZ.2024.3436941

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Abstract This article addresses an enhanced distributed outlier-resilient fusion estimation problem using an interval type-2 (IT-2) Takagi-Sugeno (T-S) fuzzy model, integrating outlier detection schemes and dimensionality reduction (DR) strategies. First, the IT-2 T-S fuzzy model is employed to handle system uncertainty and nonlinearity effectively. Then, the outlier-resilient local estimator is proposed using the zonotope-based set-membership filters (ZSMFs), where the outlier detection scheme only relies on the intersection between the predicted set and the measurement set. Furthermore, the compressed local estimate (LE) are designed when there are bandwidth constraints in sensor networks, and a novel DR strategy is proposed to design this compressed LE, where the compression matrix is determined by the Round-Robin protocol (RRP). After this, based on the compressed LEs, a distributed resilient zonotopic fusion estimator (DRZFE) is derived by the matrix-weighted fusion method. Note that the computational load of the DRZFE is reduced effectively due to the zonotope order reduction and the RRP-based DR independent of the online optimization. Moreover, the compensation of outliers and the compensating state estimate of RRP-based DR may improve the resilience of the algorithm and reduce information loss. Finally, two numerical examples are provided to validate the advantages and effectiveness of the proposed methods, and we use root-mean-square-error as the indicator to assess the estimation accuracy.
AbstractList This article addresses an enhanced distributed outlier-resilient fusion estimation problem using an interval type-2 (IT-2) Takagi-Sugeno (T-S) fuzzy model, integrating outlier detection schemes and dimensionality reduction (DR) strategies. First, the IT-2 T-S fuzzy model is employed to handle system uncertainty and nonlinearity effectively. Then, the outlier-resilient local estimator is proposed using the zonotope-based set-membership filters (ZSMFs), where the outlier detection scheme only relies on the intersection between the predicted set and the measurement set. Furthermore, the compressed local estimate (LE) are designed when there are bandwidth constraints in sensor networks, and a novel DR strategy is proposed to design this compressed LE, where the compression matrix is determined by the Round-Robin protocol (RRP). After this, based on the compressed LEs, a distributed resilient zonotopic fusion estimator (DRZFE) is derived by the matrix-weighted fusion method. Note that the computational load of the DRZFE is reduced effectively due to the zonotope order reduction and the RRP-based DR independent of the online optimization. Moreover, the compensation of outliers and the compensating state estimate of RRP-based DR may improve the resilience of the algorithm and reduce information loss. Finally, two numerical examples are provided to validate the advantages and effectiveness of the proposed methods, and we use root-mean-square-error as the indicator to assess the estimation accuracy.
Author Wang, Yidi
Peng, Zhaoxia
Yang, Yunyi
Wen, Guoguang
Xiong, Kai
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Snippet This article addresses an enhanced distributed outlier-resilient fusion estimation problem using an interval type-2 (IT-2) Takagi-Sugeno (T-S) fuzzy model,...
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SubjectTerms Anomaly detection
Dimensionality reduction
Dimensionality reduction (DR)
distributed fusion estimation
Estimation
Fuzzy systems
interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy system
Noise measurement
outlier detection
Protocols
Robot sensing systems
Round–Robin protocol (RRP)
Takagi-Sugeno model
zonotopic set-membership filter
Title Enhanced Distributed Outlier-Resilient Fusion Estimation With Novel Dimensionality Reduction Under IT-2 T-S Fuzzy System
URI https://ieeexplore.ieee.org/document/10620640
Volume 32
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