Likelihood Map Waveform Tracking Performance for GNSS-R Ocean Altimetry

Autores
Ozafrain, Santiago; Roncagliolo, Pedro Agustín; Muravchik, Carlos Horacio
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Ocean altimetry with Global Navigation Satellite Systems signals (GNSS) signals is a remote sensing technique that measures the height of the sea surface through the difference in path length of the direct and reflected signal. Code altimetry estimates this parameter by tracking the code delay after performing correlations with a GNSS signal replica. It is of limited precision due to the low signal-to-noise ratio (SNR) and narrow bandwidth of the ocean-reflected GNSS signal. However, the potential advantages of the GNSS-R systems such as high temporal resolution and spatial coverage are a motivation to improve its altimetric precision. In this article, we present a performance assessment of the Likelihood Map Waveform tracking technique, a method based on Maximum Likelihood Estimation theory that exploits the available reflected power in a more efficient way than the single tracking point methods. We use a modification of the theoretical optimal solution that achieves a better performance than previous methods. We estimate it, in terms of SNR gain, using Monte Carlo method with a detailed stochastic model of the signal, and with actual signals from the Cyclone Global Navigation Satellite System. The gain values obtained were between 1.64 and 3.66 dB in the theoretical analysis, and between 1.69 and 2.62 dB with the real data, confirming the potential of the proposed approach.
Facultad de Ingeniería
Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales
Materia
Ingeniería
Ingeniería Electrónica
GNSS+R
Low Earth Orbit (LEO)
Maximum likelihood estimation
Ocean altimetry
Remote sensing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/123755

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network_name_str SEDICI (UNLP)
spelling Likelihood Map Waveform Tracking Performance for GNSS-R Ocean AltimetryOzafrain, SantiagoRoncagliolo, Pedro AgustínMuravchik, Carlos HoracioIngenieríaIngeniería ElectrónicaGNSS+RLow Earth Orbit (LEO)Maximum likelihood estimationOcean altimetryRemote sensingOcean altimetry with Global Navigation Satellite Systems signals (GNSS) signals is a remote sensing technique that measures the height of the sea surface through the difference in path length of the direct and reflected signal. Code altimetry estimates this parameter by tracking the code delay after performing correlations with a GNSS signal replica. It is of limited precision due to the low signal-to-noise ratio (SNR) and narrow bandwidth of the ocean-reflected GNSS signal. However, the potential advantages of the GNSS-R systems such as high temporal resolution and spatial coverage are a motivation to improve its altimetric precision. In this article, we present a performance assessment of the Likelihood Map Waveform tracking technique, a method based on Maximum Likelihood Estimation theory that exploits the available reflected power in a more efficient way than the single tracking point methods. We use a modification of the theoretical optimal solution that achieves a better performance than previous methods. We estimate it, in terms of SNR gain, using Monte Carlo method with a detailed stochastic model of the signal, and with actual signals from the Cyclone Global Navigation Satellite System. The gain values obtained were between 1.64 and 3.66 dB in the theoretical analysis, and between 1.69 and 2.62 dB with the real data, confirming the potential of the proposed approach.Facultad de IngenieríaInstituto de Investigaciones en Electrónica, Control y Procesamiento de Señales2019-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf5379-5384http://sedici.unlp.edu.ar/handle/10915/123755enginfo:eu-repo/semantics/altIdentifier/issn/1939-1404info:eu-repo/semantics/altIdentifier/issn/2151-1535info:eu-repo/semantics/altIdentifier/doi/10.1109/jstars.2019.2963559info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:01:34Zoai:sedici.unlp.edu.ar:10915/123755Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:01:35.239SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Likelihood Map Waveform Tracking Performance for GNSS-R Ocean Altimetry
title Likelihood Map Waveform Tracking Performance for GNSS-R Ocean Altimetry
spellingShingle Likelihood Map Waveform Tracking Performance for GNSS-R Ocean Altimetry
Ozafrain, Santiago
Ingeniería
Ingeniería Electrónica
GNSS+R
Low Earth Orbit (LEO)
Maximum likelihood estimation
Ocean altimetry
Remote sensing
title_short Likelihood Map Waveform Tracking Performance for GNSS-R Ocean Altimetry
title_full Likelihood Map Waveform Tracking Performance for GNSS-R Ocean Altimetry
title_fullStr Likelihood Map Waveform Tracking Performance for GNSS-R Ocean Altimetry
title_full_unstemmed Likelihood Map Waveform Tracking Performance for GNSS-R Ocean Altimetry
title_sort Likelihood Map Waveform Tracking Performance for GNSS-R Ocean Altimetry
dc.creator.none.fl_str_mv Ozafrain, Santiago
Roncagliolo, Pedro Agustín
Muravchik, Carlos Horacio
author Ozafrain, Santiago
author_facet Ozafrain, Santiago
Roncagliolo, Pedro Agustín
Muravchik, Carlos Horacio
author_role author
author2 Roncagliolo, Pedro Agustín
Muravchik, Carlos Horacio
author2_role author
author
dc.subject.none.fl_str_mv Ingeniería
Ingeniería Electrónica
GNSS+R
Low Earth Orbit (LEO)
Maximum likelihood estimation
Ocean altimetry
Remote sensing
topic Ingeniería
Ingeniería Electrónica
GNSS+R
Low Earth Orbit (LEO)
Maximum likelihood estimation
Ocean altimetry
Remote sensing
dc.description.none.fl_txt_mv Ocean altimetry with Global Navigation Satellite Systems signals (GNSS) signals is a remote sensing technique that measures the height of the sea surface through the difference in path length of the direct and reflected signal. Code altimetry estimates this parameter by tracking the code delay after performing correlations with a GNSS signal replica. It is of limited precision due to the low signal-to-noise ratio (SNR) and narrow bandwidth of the ocean-reflected GNSS signal. However, the potential advantages of the GNSS-R systems such as high temporal resolution and spatial coverage are a motivation to improve its altimetric precision. In this article, we present a performance assessment of the Likelihood Map Waveform tracking technique, a method based on Maximum Likelihood Estimation theory that exploits the available reflected power in a more efficient way than the single tracking point methods. We use a modification of the theoretical optimal solution that achieves a better performance than previous methods. We estimate it, in terms of SNR gain, using Monte Carlo method with a detailed stochastic model of the signal, and with actual signals from the Cyclone Global Navigation Satellite System. The gain values obtained were between 1.64 and 3.66 dB in the theoretical analysis, and between 1.69 and 2.62 dB with the real data, confirming the potential of the proposed approach.
Facultad de Ingeniería
Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señales
description Ocean altimetry with Global Navigation Satellite Systems signals (GNSS) signals is a remote sensing technique that measures the height of the sea surface through the difference in path length of the direct and reflected signal. Code altimetry estimates this parameter by tracking the code delay after performing correlations with a GNSS signal replica. It is of limited precision due to the low signal-to-noise ratio (SNR) and narrow bandwidth of the ocean-reflected GNSS signal. However, the potential advantages of the GNSS-R systems such as high temporal resolution and spatial coverage are a motivation to improve its altimetric precision. In this article, we present a performance assessment of the Likelihood Map Waveform tracking technique, a method based on Maximum Likelihood Estimation theory that exploits the available reflected power in a more efficient way than the single tracking point methods. We use a modification of the theoretical optimal solution that achieves a better performance than previous methods. We estimate it, in terms of SNR gain, using Monte Carlo method with a detailed stochastic model of the signal, and with actual signals from the Cyclone Global Navigation Satellite System. The gain values obtained were between 1.64 and 3.66 dB in the theoretical analysis, and between 1.69 and 2.62 dB with the real data, confirming the potential of the proposed approach.
publishDate 2019
dc.date.none.fl_str_mv 2019-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/123755
url http://sedici.unlp.edu.ar/handle/10915/123755
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/1939-1404
info:eu-repo/semantics/altIdentifier/issn/2151-1535
info:eu-repo/semantics/altIdentifier/doi/10.1109/jstars.2019.2963559
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
5379-5384
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instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
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institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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