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Tipo da ReferênciaConference Proceedings
Identificador8JMKD3MGP3W34P/3MRECL5
Repositóriosid.inpe.br/mtc-m21b/2016/11.21.18.14   (acesso restrito)
Metadadossid.inpe.br/mtc-m21b/2016/11.21.18.14.48
Sitemtc-m21b.sid.inpe.br
DOI10.1109/MED.2016.7535932
ISBN978-146738345-5
Chave SecundáriaINPE--PRE/
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Chave de CitaçãoChagasWald:2016:ExDeMe
Autor1 Chagas, Ronan Arraes Jardim
2 Waldmann, Jacques
Grupo1 DSE-ETE-INPE-MCTI-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Tecnológico de Aeronáutica (ITA)
Endereço de e-Mail do Autor1 ronan.arraes@inpe.br
2 jacques@ita.br
TítuloExtrapolation of delayed measurements for fusion in a distributed sensor network
Nome do EventoMediterranean Conference on Control and Automation, 24 (MED)
Ano2016
Título do LivroProceedings
Data21-24 June
Localização do EventoAthens, Greece
Palavras-ChaveEmbedded systems, Extrapolation, Kalman filters, Sensor networks Computational burden, Computational resources, Delayed measurements, Distributed networks, Distributed sensor networks, Memory requirements, Optimal estimates, Sub-optimal algorithms.
ResumoThe 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.
Páginas1319-1324
Idiomaen
Tipo SecundárioPRE CI
AreaETES
Tamanho195 KiB
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Histórico2016-11-21 18:15:08 :: simone -> administrator :: 2016
2018-06-04 02:41:20 :: administrator -> simone :: 2016
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Data de Acesso19 out. 2020
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