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dc.contributor.author
Collado Rosell, Arturo
dc.contributor.author
Cogo, Jorge
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Areta, Javier Alberto
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Pascual, Juan Pablo
dc.date.available
2021-02-25T18:08:52Z
dc.date.issued
2020-12
dc.identifier.citation
Collado Rosell, Arturo; Cogo, Jorge; Areta, Javier Alberto; Pascual, Juan Pablo; Doppler processing in weather radar using deep learning; Institution of Engineering and Technology; Iet Signal Processing; 14; 9; 12-2020; 672-682
dc.identifier.issn
1751-9675
dc.identifier.uri
http://hdl.handle.net/11336/126655
dc.description.abstract
A deep learning approach to estimate the mean Doppler velocity and spectral width in weather radars is presented. It can operate in scenarios with and without the presence of ground clutter. The method uses a deep neural network with two branches, one for velocity and the other for spectral width estimation. Different network architectures are analysed and one is selected based on its validation performance, considering both serial and parallel implementations. Training is performed using synthetic data covering a wide range of possible scenarios. Monte Carlo realisations are used to evaluate the performance of the proposed method for different weather conditions. Results are compared against two standard methods, pulse-pair processing (PPP) for signals without ground clutter and Gaussian model adaptive processing (GMAP) for signals contaminated with ground clutter. Better estimates are obtained when comparing the proposed algorithm against GMAP and comparable results when compared against PPP. The performance is also validated using real weather data from the C-band radar RMA-12 located in San Carlos de Bariloche, Argentina. Once trained, the proposed method requires a moderate computational load and has the advantage of processing all the data at once, making it a good candidate for real-time implementations.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institution of Engineering and Technology
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
radar meteorológico
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estimación
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momentos espectrales
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redes neuronales
dc.subject.classification
Telecomunicaciones
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Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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INGENIERÍAS Y TECNOLOGÍAS
dc.title
Doppler processing in weather radar using deep learning
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-02-24T12:03:49Z
dc.journal.volume
14
dc.journal.number
9
dc.journal.pagination
672-682
dc.journal.pais
Reino Unido
dc.description.fil
Fil: Collado Rosell, Arturo. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Comision Nacional de Energia Atomica. Gerencia D/area Invest y Aplicaciones No Nucleares. Gerencia de Des. Tec. y Proyectos Especiales. Departamento de Ingenieria En Telecomunicaciones; Argentina. Universidad Nacional de Cuyo; Argentina
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Fil: Cogo, Jorge. Universidad Nacional de Río Negro; Argentina
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Fil: Areta, Javier Alberto. Universidad Nacional de Río Negro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina
dc.description.fil
Fil: Pascual, Juan Pablo. Comision Nacional de Energia Atomica. Gerencia D/area Invest y Aplicaciones No Nucleares. Gerencia de Des. Tec. y Proyectos Especiales. Departamento de Ingenieria En Telecomunicaciones; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad Nacional de Cuyo; Argentina
dc.journal.title
Iet Signal Processing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2020.0095
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1049/iet-spr.2020.0095
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