Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs

Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs

Wireless sensor networks (WSN) are often deployed in harsh environments, where electromagnetic interference, damaged sensors, or the landscape itself cause the network to suffer from faulty links and missing data. In this paper, we develop an unbiased finite impulse response (UFIR) filtering algorit...

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Main Authors: Vazquez-Olguin Miguel, Shmaiy Yuriy S., Ibarra-Manzano Oscar
Format: Article
Language:English
Published: EDP Sciences 2019-01-01
Series:MATEC Web of Conferences
Online Access:https://www.matec-conferences.org/articles/matecconf/pdf/2019/41/matecconf_cscc2019_04003.pdf
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spelling doaj-f472bae4e34b45a798b51ab6f12370b52021-02-02T07:04:53ZengEDP SciencesMATEC Web of Conferences2261-236X2019-01-012920400310.1051/matecconf/201929204003matecconf_cscc2019_04003Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNsVazquez-Olguin Miguel0Shmaiy Yuriy S.1Ibarra-Manzano Oscar21Department of Electronics Engineering, Universidad de Guanajuato1Department of Electronics Engineering, Universidad de Guanajuato1Department of Electronics Engineering, Universidad de GuanajuatoWireless sensor networks (WSN) are often deployed in harsh environments, where electromagnetic interference, damaged sensors, or the landscape itself cause the network to suffer from faulty links and missing data. In this paper, we develop an unbiased finite impulse response (UFIR) filtering algorithm for optimal consensus on estimates in distributed WSN. Simulations are provided assuming two possible scenarios with missing data. The results show that the distributed UFIR filter is more robust than the distributed Kalman filter against missing data.https://www.matec-conferences.org/articles/matecconf/pdf/2019/41/matecconf_cscc2019_04003.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Vazquez-Olguin Miguel
Shmaiy Yuriy S.
Ibarra-Manzano Oscar
spellingShingle Vazquez-Olguin Miguel
Shmaiy Yuriy S.
Ibarra-Manzano Oscar
Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs
MATEC Web of Conferences
author_facet Vazquez-Olguin Miguel
Shmaiy Yuriy S.
Ibarra-Manzano Oscar
author_sort Vazquez-Olguin Miguel
title Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs
title_short Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs
title_full Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs
title_fullStr Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs
title_full_unstemmed Development of a Robust UFIR Filter with Consensus on Estimates for Missing Data and unknown noise statistics over WSNs
title_sort development of a robust ufir filter with consensus on estimates for missing data and unknown noise statistics over wsns
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2019-01-01
description Wireless sensor networks (WSN) are often deployed in harsh environments, where electromagnetic interference, damaged sensors, or the landscape itself cause the network to suffer from faulty links and missing data. In this paper, we develop an unbiased finite impulse response (UFIR) filtering algorithm for optimal consensus on estimates in distributed WSN. Simulations are provided assuming two possible scenarios with missing data. The results show that the distributed UFIR filter is more robust than the distributed Kalman filter against missing data.
url https://www.matec-conferences.org/articles/matecconf/pdf/2019/41/matecconf_cscc2019_04003.pdf
work_keys_str_mv AT vazquezolguinmiguel developmentofarobustufirfilterwithconsensusonestimatesformissingdataandunknownnoisestatisticsoverwsns
AT shmaiyyuriys developmentofarobustufirfilterwithconsensusonestimatesformissingdataandunknownnoisestatisticsoverwsns
AT ibarramanzanooscar developmentofarobustufirfilterwithconsensusonestimatesformissingdataandunknownnoisestatisticsoverwsns
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