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Singular spectrum analysis for modeling seasonal signals from GPS time series / Q. Chen in Journal of geodynamics, vol 72 (December 2013)  

Public [article]  inJournal of geodynamics > vol 72 (December 2013) . - pp 25 - 35 Titre : Singular spectrum analysis for modeling seasonal signals from GPS time series Type de document : Article/Communication Auteurs : Q. Chen, Auteur ; Tonie M. van Dam, Auteur ; Nico Sneeuw, Auteur ; Xavier Collilieux , Auteur ; M. Weigelt, Auteur ; Paul Rebischung , Auteur Année de publication : 2013 Article en page(s) : pp 25 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse spectrale [Termes IGN] compensation par moindres carrés [Termes IGN] filtre de Kalman [Termes IGN] matrice de covariance [Termes IGN] série temporelle [Termes IGN] signal GPS [Termes IGN] variation saisonnière [Vedettes matières IGN] Traitement de données GNSS Résumé : (auteur) Seasonal signals in GPS time series are of great importance for understanding the evolution of regional mass fluctuations, i.e., ice, hydrology, and ocean mass. Conventionally these signals (quasi-annual and semi-annual signals) are modeled by least-squares fitting harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e., they will have a time-variable amplitude and phase. Recently, Davis et al. (2012) proposed a Kalman filter based approach to capture the stochastic seasonal behavior of geodetic time series. Singular Spectrum Analysis (SSA) is a non-parametric method, which uses time domain data to extract information from short and noisy time series without a priori knowledge of the dynamics affecting the time series. A prominent benefit is that trends obtained in this way are not necessarily linear. Further, true oscillations can be amplitude and phase modulated. In this work, we will assess the value of SSA for extracting time-variable seasonal signals from GPS time series. We compare our SSA-based results to those obtained using (1) least-squares analysis and (2) Kalman filtering. Our results demonstrate that SSA is a viable and complementary tool for extracting modulated oscillations from GPS time series. Numéro de notice : A2013-512 Affiliation des auteurs : LASTIG LAREG+Ext (2012-mi2018) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jog.2013.05.005 Date de publication en ligne : 28/06/2013 En ligne : http://dx.doi.org/10.1016/j.jog.2013.05.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80232 [article]