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Long-term prediction of polar motion using a combined SSA and ARMA model / Y. Shen in Journal of geodesy, vol 92 n° 3 (March 2018)  

Public [article]  inJournal of geodesy > vol 92 n° 3 (March 2018) . - pp 333 - 343 Titre : Long-term prediction of polar motion using a combined SSA and ARMA model Type de document : Article/Communication Auteurs : Y. Shen, Auteur ; Jinyun Guo, Auteur ; X. Liu, Auteur ; Qiaoli Kong, Auteur ; Linxi Guo, Auteur ; Li Wang, Auteur Année de publication : 2018 Article en page(s) : pp 333 - 343 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse de spectre singulier [Termes IGN] analyse en composantes principales [Termes IGN] modèle de simulation [Termes IGN] mouvement du pôle Mots-clés libres : modèle ARMA Résumé : (Auteur) To meet the need for real-time and high-accuracy predictions of polar motion (PM), the singular spectrum analysis (SSA) and the autoregressive moving average (ARMA) model are combined for short- and long-term PM prediction. According to the SSA results for PM and the SSA prediction algorithm, the principal components of PM were predicted by SSA, and the remaining components were predicted by the ARMA model. In applying this proposed method, multiple sets of PM predictions were made with lead times of two years, based on an IERS 08 C04 series. The observations and predictions of the principal components correlated well, and the SSA + ARMA model effectively predicted the PM. For 360-day lead time predictions, the root-mean-square errors (RMSEs) of PMx and PMy were 20.67 and 20.42 mas, respectively, which were less than the 24.46 and 24.78 mas predicted by IERS Bulletin A. The RMSEs of PMx and PMy in the 720-day lead time predictions were 28.61 and 27.95 mas, respectively. Numéro de notice : A2018-061 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-017-1065-3 Date de publication en ligne : 12/09/2017 En ligne : https://doi.org/10.1007/s00190-017-1065-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89397 [article]

A greedy-based multiquadric method for LiDAR-derived ground data reduction / Chuanfa Chen in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 102 (April 2015) . - pp 110 - 121 Titre : A greedy-based multiquadric method for LiDAR-derived ground data reduction Type de document : Article/Communication Auteurs : Chuanfa Chen, Auteur ; Changqing Yan, Auteur ; Xuewei Cao, Auteur ; Jinyun Guo, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 110 - 121 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données de terrain [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] interpolation [Termes IGN] lissage de données [Termes IGN] modèle numérique de surface [Termes IGN] réduction Résumé : (auteur) A new greedy-based multiquadric method (MQ-G) has been developed to perform LiDAR-derived ground data reduction by selecting a certain amount of significant terrain points from the raw dataset to keep the accuracy of the constructed DEMs as high as possible, while maximally retaining terrain features. In the process of MQ-G, the significant terrain points were selected with an iterative process. First, the points with the maximum and minimum elevations were selected as the initial significant points. Next, a smoothing MQ was employed to perform an interpolation with the selected critical points. Then, the importance of all candidate points was assessed by interpolation error (i.e. the absolute difference between the interpolated and actual elevations). Lastly, the most significant point in the current iteration was selected and used for point selection in the next iteration. The process was repeated until the number of selected points reached a pre-set level or no point was found to have the interpolation error exceeding a user-specified accuracy tolerance. In order to avoid the huge computing cost, a new technique was presented to quickly solve the systems of MQ equations in the global interpolation process, and then the global MQ was replaced with the local one when a certain amount of critical points were selected. Four study sites with different morphologies (i.e. flat, undulating, hilly and mountainous terrains) were respectively employed to comparatively analyze the performances of MQ-G and the classical data selection methods including maximum z-tolerance (Max-Z) and the random method for reducing LiDAR-derived ground datasets. Results show that irrespective of the number of selected critical points and terrain characteristics, MQ-G is always more accurate than the other methods for DEM construction. Moreover, MQ-G has a better ability of preserving terrain feature lines, especially for the undulating and hilly terrains. Numéro de notice : A2015-693 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.01.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.01.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78327 [article]