A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development of multisensor fusion technolog...

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Main Authors: Wangyan Li, Zidong Wang, Guoliang Wei, Lifeng Ma, Jun Hu, Derui Ding
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2015/683701
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author Wangyan Li
Zidong Wang
Guoliang Wei
Lifeng Ma
Jun Hu
Derui Ding
author_facet Wangyan Li
Zidong Wang
Guoliang Wei
Lifeng Ma
Jun Hu
Derui Ding
author_sort Wangyan Li
collection DOAJ
description Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development of multisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.
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spelling doaj-art-8c1b8e4a7e654f8bb1718dbada8c1c102025-02-03T05:50:50ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2015-01-01201510.1155/2015/683701683701A Survey on Multisensor Fusion and Consensus Filtering for Sensor NetworksWangyan Li0Zidong Wang1Guoliang Wei2Lifeng Ma3Jun Hu4Derui Ding5Business School, University of Shanghai for Science and Technology, Shanghai 200093, ChinaDepartment of Computer Science, Brunel University London, Uxbridge, Middlesex UB8 3PH, UKSchool of Optical-Electrical and Computer Engineering, Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaDepartment of Applied Mathematics, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Optical-Electrical and Computer Engineering, Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, ChinaMultisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development of multisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.http://dx.doi.org/10.1155/2015/683701
spellingShingle Wangyan Li
Zidong Wang
Guoliang Wei
Lifeng Ma
Jun Hu
Derui Ding
A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
Discrete Dynamics in Nature and Society
title A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
title_full A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
title_fullStr A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
title_full_unstemmed A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
title_short A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks
title_sort survey on multisensor fusion and consensus filtering for sensor networks
url http://dx.doi.org/10.1155/2015/683701
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