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SVR and ARIMA models as machine learning solutions for solving the latency problem in real-time clock corrections

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SVR and ARIMA models as machine learning solutions for solving the latency problem in real-time clock corrections

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Qafisheh, M.; Martín Furones, ÁE.; Capilla, RM.; Anquela Julián, AB. (2022). SVR and ARIMA models as machine learning solutions for solving the latency problem in real-time clock corrections. GPS Solutions. 26(3):1-14. https://doi.org/10.1007/s10291-022-01270-y

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Título: SVR and ARIMA models as machine learning solutions for solving the latency problem in real-time clock corrections
Autor: Qafisheh, Mutaz Martín Furones, Ángel Esteban Capilla, Raquel M. Anquela Julián, Ana Belén
Entidad UPV: Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica - Escola Tècnica Superior d'Enginyeria Geodèsica, Cartogràfica i Topogràfica
Fecha difusión:
Resumen:
[EN] Real-time precise point positioning (PPP) has become a prevalent technique in global navigation satellite systems (GNSS). However, GNSS real-time users must receive space state representation (SSR) products to correct ...[+]
Palabras clave: Precise point positioning , Real-time positioning , Support vector regression , Autoregressive integrated moving average , Clock corrections
Derechos de uso: Reconocimiento (by)
Fuente:
GPS Solutions. (issn: 1080-5370 )
DOI: 10.1007/s10291-022-01270-y
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s10291-022-01270-y
Agradecimientos:
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. We greatly appreciate the eforts of the IGS, Analysis and Data Centers, and tracking station managers for generating high-quality data ...[+]
Tipo: Artículo

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