State Fusion of Decentralized Optimal Unbiased FIR Filters

The paper presents a decentralized fusion strategy based on the optimal unbiased finite impulse response (OUFIR) filter for discrete systems with correlated process and measurement noise. We extend OUFIR filter to apply in the model with control inputs. Taking it as local filters, cross covariance b...

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Main Authors: Xuefeng Fan, Fei Liu
Format: Article
Language:English
Published: Wiley 2018-01-01
Series:Journal of Electrical and Computer Engineering
Online Access:http://dx.doi.org/10.1155/2018/1505137
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author Xuefeng Fan
Fei Liu
author_facet Xuefeng Fan
Fei Liu
author_sort Xuefeng Fan
collection DOAJ
description The paper presents a decentralized fusion strategy based on the optimal unbiased finite impulse response (OUFIR) filter for discrete systems with correlated process and measurement noise. We extend OUFIR filter to apply in the model with control inputs. Taking it as local filters, cross covariance between any two is calculated; then it is expressed to the fast iterative form. Finally based on cross covariance, optimal weights are utilized to fuse local estimates and the overall outcome is obtained. The numerical examples show that the proposed filter exhibits better robustness against temporary modeling uncertainties than the fusion Kalman filter used commonly.
format Article
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2090-0155
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publishDate 2018-01-01
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series Journal of Electrical and Computer Engineering
spelling doaj-art-abb3abe3c3474cd5acf059ece9a83e412025-08-20T02:20:13ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552018-01-01201810.1155/2018/15051371505137State Fusion of Decentralized Optimal Unbiased FIR FiltersXuefeng Fan0Fei Liu1Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, ChinaKey Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, ChinaThe paper presents a decentralized fusion strategy based on the optimal unbiased finite impulse response (OUFIR) filter for discrete systems with correlated process and measurement noise. We extend OUFIR filter to apply in the model with control inputs. Taking it as local filters, cross covariance between any two is calculated; then it is expressed to the fast iterative form. Finally based on cross covariance, optimal weights are utilized to fuse local estimates and the overall outcome is obtained. The numerical examples show that the proposed filter exhibits better robustness against temporary modeling uncertainties than the fusion Kalman filter used commonly.http://dx.doi.org/10.1155/2018/1505137
spellingShingle Xuefeng Fan
Fei Liu
State Fusion of Decentralized Optimal Unbiased FIR Filters
Journal of Electrical and Computer Engineering
title State Fusion of Decentralized Optimal Unbiased FIR Filters
title_full State Fusion of Decentralized Optimal Unbiased FIR Filters
title_fullStr State Fusion of Decentralized Optimal Unbiased FIR Filters
title_full_unstemmed State Fusion of Decentralized Optimal Unbiased FIR Filters
title_short State Fusion of Decentralized Optimal Unbiased FIR Filters
title_sort state fusion of decentralized optimal unbiased fir filters
url http://dx.doi.org/10.1155/2018/1505137
work_keys_str_mv AT xuefengfan statefusionofdecentralizedoptimalunbiasedfirfilters
AT feiliu statefusionofdecentralizedoptimalunbiasedfirfilters