Consensus Filtering Algorithm with Incomplete Measurements

In order to improve the estimate performance of distributed sensor networks, we introduce an improved consensus-based distributed filtering algorithm. Firstly, sensors obtain the local estimates using their own measurements. Then through utilizing the data of neighbor nodes to update the local estim...

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Main Authors: Yungang Wu, Zhenmin Tang
Format: Article
Language:English
Published: Wiley 2015-11-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2015/131247
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author Yungang Wu
Zhenmin Tang
author_facet Yungang Wu
Zhenmin Tang
author_sort Yungang Wu
collection DOAJ
description In order to improve the estimate performance of distributed sensor networks, we introduce an improved consensus-based distributed filtering algorithm. Firstly, sensors obtain the local estimates using their own measurements. Then through utilizing the data of neighbor nodes to update the local estimates, estimates in the networks can reach dynamic average consensus. Based on studying the value of consensus step size, we give a sufficient condition for the convergence of the algorithm and discusses the influences of the consensus step size and the detection probability on the accuracy and consensus of estimation. The numerical simulation demonstrates that the algorithm proposed in this paper can improve the accuracy and consensus of estimation, and it is more robust with incomplete measurements.
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institution Kabale University
issn 1550-1477
language English
publishDate 2015-11-01
publisher Wiley
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-109125908cda49a2899e644bf159ea1a2025-02-03T06:45:16ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-11-011110.1155/2015/131247131247Consensus Filtering Algorithm with Incomplete MeasurementsYungang WuZhenmin TangIn order to improve the estimate performance of distributed sensor networks, we introduce an improved consensus-based distributed filtering algorithm. Firstly, sensors obtain the local estimates using their own measurements. Then through utilizing the data of neighbor nodes to update the local estimates, estimates in the networks can reach dynamic average consensus. Based on studying the value of consensus step size, we give a sufficient condition for the convergence of the algorithm and discusses the influences of the consensus step size and the detection probability on the accuracy and consensus of estimation. The numerical simulation demonstrates that the algorithm proposed in this paper can improve the accuracy and consensus of estimation, and it is more robust with incomplete measurements.https://doi.org/10.1155/2015/131247
spellingShingle Yungang Wu
Zhenmin Tang
Consensus Filtering Algorithm with Incomplete Measurements
International Journal of Distributed Sensor Networks
title Consensus Filtering Algorithm with Incomplete Measurements
title_full Consensus Filtering Algorithm with Incomplete Measurements
title_fullStr Consensus Filtering Algorithm with Incomplete Measurements
title_full_unstemmed Consensus Filtering Algorithm with Incomplete Measurements
title_short Consensus Filtering Algorithm with Incomplete Measurements
title_sort consensus filtering algorithm with incomplete measurements
url https://doi.org/10.1155/2015/131247
work_keys_str_mv AT yungangwu consensusfilteringalgorithmwithincompletemeasurements
AT zhenmintang consensusfilteringalgorithmwithincompletemeasurements