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Consensus-Based Filter for Distributed Sensor Networks with Colored Measurement Noise

Consensus filtering is an effective method for distributed state estimation of distributed sensor networks and the assumption of white measurement noise is widely used. However, when the measurement noise is colored, the traditional consensus filter cannot work well. In this paper, we first propose...

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Detalles Bibliográficos
Autores principales: Wang, Jinran, Dong, Peng, Jing, Zhongliang, Cheng, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263659/
https://www.ncbi.nlm.nih.gov/pubmed/30380677
http://dx.doi.org/10.3390/s18113678
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author Wang, Jinran
Dong, Peng
Jing, Zhongliang
Cheng, Jin
author_facet Wang, Jinran
Dong, Peng
Jing, Zhongliang
Cheng, Jin
author_sort Wang, Jinran
collection PubMed
description Consensus filtering is an effective method for distributed state estimation of distributed sensor networks and the assumption of white measurement noise is widely used. However, when the measurement noise is colored, the traditional consensus filter cannot work well. In this paper, we first propose a consensus-based distributed filter for colored measurement noise by augmenting the state to include the colored measurement noise. To improve the efficiency of the filter, only local colored measurement noise is integrated into the augmented state for each local filter. Furthermore, another consensus-based distributed filter based on measurement differencing scheme is developed to eliminate the ill-conditioned computations of the augmented state approach. In addition, this method does not need to augment the state and thus has lower dimension than the augmented state filter. Simulation results demonstrate the superiority of the proposed methods.
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spelling pubmed-62636592018-12-12 Consensus-Based Filter for Distributed Sensor Networks with Colored Measurement Noise Wang, Jinran Dong, Peng Jing, Zhongliang Cheng, Jin Sensors (Basel) Article Consensus filtering is an effective method for distributed state estimation of distributed sensor networks and the assumption of white measurement noise is widely used. However, when the measurement noise is colored, the traditional consensus filter cannot work well. In this paper, we first propose a consensus-based distributed filter for colored measurement noise by augmenting the state to include the colored measurement noise. To improve the efficiency of the filter, only local colored measurement noise is integrated into the augmented state for each local filter. Furthermore, another consensus-based distributed filter based on measurement differencing scheme is developed to eliminate the ill-conditioned computations of the augmented state approach. In addition, this method does not need to augment the state and thus has lower dimension than the augmented state filter. Simulation results demonstrate the superiority of the proposed methods. MDPI 2018-10-29 /pmc/articles/PMC6263659/ /pubmed/30380677 http://dx.doi.org/10.3390/s18113678 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Jinran
Dong, Peng
Jing, Zhongliang
Cheng, Jin
Consensus-Based Filter for Distributed Sensor Networks with Colored Measurement Noise
title Consensus-Based Filter for Distributed Sensor Networks with Colored Measurement Noise
title_full Consensus-Based Filter for Distributed Sensor Networks with Colored Measurement Noise
title_fullStr Consensus-Based Filter for Distributed Sensor Networks with Colored Measurement Noise
title_full_unstemmed Consensus-Based Filter for Distributed Sensor Networks with Colored Measurement Noise
title_short Consensus-Based Filter for Distributed Sensor Networks with Colored Measurement Noise
title_sort consensus-based filter for distributed sensor networks with colored measurement noise
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6263659/
https://www.ncbi.nlm.nih.gov/pubmed/30380677
http://dx.doi.org/10.3390/s18113678
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