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1. Identity statement
Reference TypeBook Section
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3RFKBH2
Repositorysid.inpe.br/mtc-m21c/2018/07.18.16.21   (restricted access)
Last Update2020:02.10.17.16.43 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2018/07.18.16.21.06
Metadata Last Update2020:02.10.17.16.44 (UTC) simone
Secondary KeyINPE--PRE/
ISBN978-3-030-16077-7
Citation KeyPenhaNetoCampShig:2019:ImPrUA
TitleImage processing for UAV autonomous navigation applying self-configuring neural network
Year2019
Access Date2024, May 08
Secondary TypePRE LI
Number of Files1
Size701 KiB
2. Context
Author1 Penha Neto, Gerson da
2 Campos Velho, Haroldo Fraga de
3 Shiguemori, Elcio Hideki
Resume Identifier1
2 8JMKD3MGP5W/3C9JHC3
Group1 CAP-COMP-SESPG-INPE-MCTIC-GOV-BR
2 LABAC-COCTE-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 gerson.penha@inpe.br
2 haroldo.camposvelho@inpe.br
EditorConstanda, Christian
Harris, Paul
Book TitleIntegral methods in science and engineering
PublisherSpringer
CityBrighton, UK
Pages321-342
History (UTC)2018-07-18 16:21:17 :: simone -> administrator :: 2018
2020-02-10 17:14:00 :: administrator -> simone :: 2018
2020-02-10 17:16:43 :: simone :: 2018 -> 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsUnmanned aerial vehicles
Kalman filter
artificial neural networks
AbstractApplication and development of Unmanned Aerial Vehicles (UAV) have had a rapid growth. The flight control of these aircarfts can be performed remotely or autonomously. There are different strategies for the UAV autonomous navigation. The positioning estimation can be done by using inertial sensors and General Navigation Satellite Systems (GNSS). The use of the GNSS signal can present some difficulties: natural or not natural interference. An alternative for positioning adjustment is to use a data fusion from different sensors by a Kalman filter. A supervised artificial network (ANN) is trained to emulate the filter for reducing the computational effort. An automatic best topology for the neural network is obtained by minimizing a functional by a new meta-heurisc called Multi-Particle Collision Algorithm (MPCA). Our results show similar accuracy between the ANN and the Kalman filter, with better processing performance to the neural network.
AreaCOMP
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Image processing for...
Arrangement 2urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > Image processing for...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
Languageen
Target FilePenha_image.pdf
User Groupsimone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ESGTTP
8JMKD3MGPCW/3F2PHGS
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition format issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarymark serieseditor seriestitle session shorttitle sponsor subject tertiarymark tertiarytype translator url volume
7. Description control
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