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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3MRECL5
Repositorysid.inpe.br/mtc-m21b/2016/11.21.18.14   (restricted access)
Last Update2016:11.21.18.14.48 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21b/2016/11.21.18.14.48
Metadata Last Update2021:02.11.20.58.01 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1109/MED.2016.7535932
ISBN978-146738345-5
Citation KeyChagasWald:2016:ExDeMe
TitleExtrapolation of delayed measurements for fusion in a distributed sensor network
Year2016
Access Date2024, Apr. 25
Secondary TypePRE CI
Number of Files1
Size195 KiB
2. Context
Author1 Chagas, Ronan Arraes Jardim
2 Waldmann, Jacques
Group1 DSE-ETE-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Tecnológico de Aeronáutica (ITA)
Author e-Mail Address1 ronan.arraes@inpe.br
2 jacques@ita.br
Conference NameMediterranean Conference on Control and Automation, 24 (MED)
Conference LocationAthens, Greece
Date21-24 June
Pages1319-1324
Book TitleProceedings
History (UTC)2016-11-21 18:15:08 :: simone -> administrator :: 2016
2021-02-11 20:58:01 :: administrator -> simone :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsEmbedded systems
Extrapolation
Kalman filters
Sensor networks Computational burden
Computational resources
Delayed measurements
Distributed networks
Distributed sensor networks
Memory requirements
Optimal estimates
Sub-optimal algorithms
AbstractThe measurement extrapolation (ME) algorithm was devised to fuse delayed measurements in the Kalman filter. It is a suboptimal algorithm that greatly reduces the computational burden of the optimal Reiterated Kalman Filter (RKF). ME can be used in embedded systems that lack the required computational resources to compute the optimal estimate. However, it has not been extended yet to be applied in a distributed sensor network. Furthermore, it is verified here that the original ME algorithm provides a biased estimate, which can degrade the estimation accuracy. Thus, this work proposes to extend ME to fuse delayed measurements received by nodes in a distributed network, and to remove the bias using Bayesian concepts, improving the accuracy of the novel method. The ME computational burden and memory needs are theoretically analyzed and compared to those of the RKF. Finally, simulations of a simplified distributed network are presented to measure the performance of the new algorithm with respect to RKF and to validate the theoretical analysis. The results show that ME can provide an estimate with acceptable accuracy whereas the computational burden is greatly decreased and the memory requirements are only slightly increased compared to RKF.
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4. Conditions of access and use
Languageen
Target Filechagas_extrapolation.pdf
User Groupsimone
Reader Groupadministrator
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Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGPCW/446AUSL
Citing Item Listsid.inpe.br/bibdigital/2021/02.11.20.54 1
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
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