Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.

Detalhes bibliográficos
Ano de defesa: 2004
Autor(a) principal: Julio Cesar Bolzani de Campos Ferreira
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Instituto Tecnológico de Aeronáutica
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Filtros de Kalman
Fusão de multisensor
Veículos de lançamento
Estimação de sistemas
Radar
Engenharia aeroespacial
Engenharia eletrônica
Link de acesso: http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=679
Resumo: This work focuses on tracking launch vehicles with multiple radar sites and proposes a data fusion strategy based on the Covariance Intersection (CI) method. At each site, multiple models are embedded in a Kalman filter or locally estimate position, velocity, and acceleration using a de-biased measurement transformation from spherical to cartesian coordinates. The estimation of position, velocity, and acceleration of moving object based on radar measurements is critical in applications such as air traffic control, surveillance systems, and orbital vehicles launching among many others. However, this work will focus on the conditions observed at Alcântara Launch Center, where two radars located at distinct sites provide the trajectory coverage. All simulations presented herein make use of actual data obtained from a VS30 sounding rocket launch at Alcântara Launch Center in February, 2000. Impact point prediction is also assessed, and considers uncertainties inferred form the computed covariance matrix, culminating in an ellipsoidal impact area with a given impact probability.
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network_name_str Biblioteca Digital de Teses e Dissertações do ITA
spelling Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.Filtros de KalmanFusão de multisensorVeículos de lançamentoEstimação de sistemasRadarEngenharia aeroespacialEngenharia eletrônicaThis work focuses on tracking launch vehicles with multiple radar sites and proposes a data fusion strategy based on the Covariance Intersection (CI) method. At each site, multiple models are embedded in a Kalman filter or locally estimate position, velocity, and acceleration using a de-biased measurement transformation from spherical to cartesian coordinates. The estimation of position, velocity, and acceleration of moving object based on radar measurements is critical in applications such as air traffic control, surveillance systems, and orbital vehicles launching among many others. However, this work will focus on the conditions observed at Alcântara Launch Center, where two radars located at distinct sites provide the trajectory coverage. All simulations presented herein make use of actual data obtained from a VS30 sounding rocket launch at Alcântara Launch Center in February, 2000. Impact point prediction is also assessed, and considers uncertainties inferred form the computed covariance matrix, culminating in an ellipsoidal impact area with a given impact probability.Instituto Tecnológico de AeronáuticaJacques WaldmannAristóteles de Sousa CarvalhoJulio Cesar Bolzani de Campos Ferreira2004-12-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=679reponame:Biblioteca Digital de Teses e Dissertações do ITAinstname:Instituto Tecnológico de Aeronáuticainstacron:ITAenginfo:eu-repo/semantics/openAccessapplication/pdf2019-02-02T14:01:53Zoai:agregador.ibict.br.BDTD_ITA:oai:ita.br:679http://oai.bdtd.ibict.br/requestopendoar:null2020-05-28 19:33:57.228Biblioteca Digital de Teses e Dissertações do ITA - Instituto Tecnológico de Aeronáuticatrue
dc.title.none.fl_str_mv Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.
title Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.
spellingShingle Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.
Julio Cesar Bolzani de Campos Ferreira
Filtros de Kalman
Fusão de multisensor
Veículos de lançamento
Estimação de sistemas
Radar
Engenharia aeroespacial
Engenharia eletrônica
title_short Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.
title_full Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.
title_fullStr Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.
title_full_unstemmed Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.
title_sort Data fusion and multiple models filtering for launch vehicle tracking and impact point prediction: the Alcântara case.
author Julio Cesar Bolzani de Campos Ferreira
author_facet Julio Cesar Bolzani de Campos Ferreira
author_role author
dc.contributor.none.fl_str_mv Jacques Waldmann
Aristóteles de Sousa Carvalho
dc.contributor.author.fl_str_mv Julio Cesar Bolzani de Campos Ferreira
dc.subject.por.fl_str_mv Filtros de Kalman
Fusão de multisensor
Veículos de lançamento
Estimação de sistemas
Radar
Engenharia aeroespacial
Engenharia eletrônica
topic Filtros de Kalman
Fusão de multisensor
Veículos de lançamento
Estimação de sistemas
Radar
Engenharia aeroespacial
Engenharia eletrônica
dc.description.none.fl_txt_mv This work focuses on tracking launch vehicles with multiple radar sites and proposes a data fusion strategy based on the Covariance Intersection (CI) method. At each site, multiple models are embedded in a Kalman filter or locally estimate position, velocity, and acceleration using a de-biased measurement transformation from spherical to cartesian coordinates. The estimation of position, velocity, and acceleration of moving object based on radar measurements is critical in applications such as air traffic control, surveillance systems, and orbital vehicles launching among many others. However, this work will focus on the conditions observed at Alcântara Launch Center, where two radars located at distinct sites provide the trajectory coverage. All simulations presented herein make use of actual data obtained from a VS30 sounding rocket launch at Alcântara Launch Center in February, 2000. Impact point prediction is also assessed, and considers uncertainties inferred form the computed covariance matrix, culminating in an ellipsoidal impact area with a given impact probability.
description This work focuses on tracking launch vehicles with multiple radar sites and proposes a data fusion strategy based on the Covariance Intersection (CI) method. At each site, multiple models are embedded in a Kalman filter or locally estimate position, velocity, and acceleration using a de-biased measurement transformation from spherical to cartesian coordinates. The estimation of position, velocity, and acceleration of moving object based on radar measurements is critical in applications such as air traffic control, surveillance systems, and orbital vehicles launching among many others. However, this work will focus on the conditions observed at Alcântara Launch Center, where two radars located at distinct sites provide the trajectory coverage. All simulations presented herein make use of actual data obtained from a VS30 sounding rocket launch at Alcântara Launch Center in February, 2000. Impact point prediction is also assessed, and considers uncertainties inferred form the computed covariance matrix, culminating in an ellipsoidal impact area with a given impact probability.
publishDate 2004
dc.date.none.fl_str_mv 2004-12-15
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/masterThesis
status_str publishedVersion
format masterThesis
dc.identifier.uri.fl_str_mv http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=679
url http://www.bd.bibl.ita.br/tde_busca/arquivo.php?codArquivo=679
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Instituto Tecnológico de Aeronáutica
publisher.none.fl_str_mv Instituto Tecnológico de Aeronáutica
dc.source.none.fl_str_mv reponame:Biblioteca Digital de Teses e Dissertações do ITA
instname:Instituto Tecnológico de Aeronáutica
instacron:ITA
reponame_str Biblioteca Digital de Teses e Dissertações do ITA
collection Biblioteca Digital de Teses e Dissertações do ITA
instname_str Instituto Tecnológico de Aeronáutica
instacron_str ITA
institution ITA
repository.name.fl_str_mv Biblioteca Digital de Teses e Dissertações do ITA - Instituto Tecnológico de Aeronáutica
repository.mail.fl_str_mv
subject_por_txtF_mv Filtros de Kalman
Fusão de multisensor
Veículos de lançamento
Estimação de sistemas
Radar
Engenharia aeroespacial
Engenharia eletrônica
_version_ 1706804988922036224

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