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Fusion of GNSS and InSAR time series using the improved STRE model: applications to the San Francisco bay area and Southern California / Huineng Yan in Journal of geodesy, vol 96 n° 7 (July 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 7 (July 2022) . - n° 47 Titre : Fusion of GNSS and InSAR time series using the improved STRE model: applications to the San Francisco bay area and Southern California Type de document : Article/Communication Auteurs : Huineng Yan, Auteur ; Wujiao Dai, Auteur ; Lei Xie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Californie (Etats-Unis) [Termes IGN] déformation de la croute terrestre [Termes IGN] données GNSS [Termes IGN] faille géologique [Termes IGN] filtrage spatiotemporel [Termes IGN] fusion de données [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] modélisation spatiale [Termes IGN] rééchantillonnage [Termes IGN] série temporelle Résumé : (auteur) The spatio-temporal random effects (STRE) model is a classic dynamic filtering model, which can be used to fuse GNSS and InSAR deformation data. The STRE model uses a certain time span of high spatial resolution Interferometric Synthetic Aperture Radar (InSAR) time series data to establish a spatial model and then obtain a deformation result with high spatio-temporal resolution through the state transition equation recursively in time domain. Combined with the Kalman filter, the STRE model is continuously updated and modified in time domain to obtain higher accuracy result. However, it relies heavily on the prior information and personal experience to establish an accurate spatial model. To the authors' knowledge, there are no publications which use the STRE model with multiple sets of different deformation monitoring data to verify its applicability and reliability. Here, we propose an improved STRE model to automatically establish accurate spatial model to improve the STRE model, then apply it to the fusion of GNSS and InSAR deformation data in the San Francisco Bay Area covering approximately 6000 km2 and in Southern California covering approximately 100,000 km2. Our experimental results show that the improved STRE model can achieve good fusion effects in both study areas. For internal inspection, the average error RMS values in the two regions are 0.13 mm and 0.06 mm for InSAR and 2.4 and 2.8 mm for GNSS, respectively; for Jackknife cross-validation, the average error RMS values are 6.0 and 1.3 mm for InSAR and 4.3 and 4.8 mm for GNSS in the two regions, respectively. We find that the deformation rate calculated from the fusion results is highly consistent with the existing studies, the significant difference in the deformation rate on both sides of the major faults in the two regions can be clearly seen, and the area with abnormal deformation rate corresponds well to the actual situation. These results indicate that the improved STRE model can reduce the reliance on prior information and personal experience, realize the effective fusion of GNSS and InSAR deformation data in different regions, and also has the advantages of high accuracy and strong applicability. Numéro de notice : A2022-553 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/s00190-022-01636-7 Date de publication en ligne : 05/07/2022 En ligne : https://doi.org/10.1007/s00190-022-01636-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101165 [article]

Mixed geographically and temporally weighted regression for spatio-temporal deformation modelling / Zhijia Yang in Survey review, vol 54 n° 385 (July 2022)  

Public [article]  inSurvey review > vol 54 n° 385 (July 2022) . - pp 290 - 300 Titre : Mixed geographically and temporally weighted regression for spatio-temporal deformation modelling Type de document : Article/Communication Auteurs : Zhijia Yang, Auteur ; Wujiao Dai, Auteur ; Wenkun Yu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 290 - 300 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie [Termes IGN] auscultation d'ouvrage [Termes IGN] barrage [Termes IGN] déformation d'édifice [Termes IGN] méthode fondée sur le noyau [Termes IGN] modèle de simulation [Termes IGN] modélisation spatio-temporelle [Termes IGN] régression géographiquement pondérée [Termes IGN] surveillance d'ouvrage Résumé : (auteur) When the regression coefficient of independent variable has both global stationarity and spatio-temporal non-stationarity properties, the deformation model based on the geographically and temporally weighted regression (GTWR) will no longer be applicable. In order to resolve this problem, we propose an improved method to establish the spatio-temporal deformation model using mixed geographically and temporally weighted regression (MGTWR). In this method, both the global regression coefficient and the variable regression coefficient are selected for regression coefficient hypothesis test, and the local linear two-step estimation method is used to fit the MGTWR model. A dam deformation modelling example shows that the MGTWR model improves the average prediction accuracy by 57.6% compared to the GTWR model when the regression coefficients have both global stationarity and spatio-temporal non-stationarity properties. Numéro de notice : A2022-534 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1935578 Date de publication en ligne : 10/06/2021 En ligne : https://doi.org/10.1080/00396265.2021.1935578 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101090 [article]

Systematic error mitigation in multi-GNSS positioning based on semiparametric estimation / Wenkun Yu in Journal of geodesy, vol 91 n° 12 (December 2017)  

Public [article]  inJournal of geodesy > vol 91 n° 12 (December 2017) . - pp 1491 - 1502 Titre : Systematic error mitigation in multi-GNSS positioning based on semiparametric estimation Type de document : Article/Communication Auteurs : Wenkun Yu, Auteur ; Xiaoli Ding, Auteur ; Wujiao Dai, Auteur ; Wu Chen, Auteur Année de publication : 2017 Article en page(s) : pp 1491 - 1502 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] analyse de variance [Termes IGN] atténuation [Termes IGN] erreur systématique [Termes IGN] modèle stochastique [Termes IGN] positionnement par GNSS [Termes IGN] précision du positionnement Résumé : (Auteur) Joint use of observations from multiple global navigation satellite systems (GNSS) is advantageous in high-accuracy positioning. However, systematic errors in the observations can significantly impact on the positioning accuracy if such errors cannot be properly mitigated. The errors can distort least squares estimations and also affect the results of variance component estimation that is frequently used to determine the stochastic model when observations from multiple GNSS are used. We present an approach that is based on the concept of semiparametric estimation for mitigating the effects of the systematic errors. Experimental results based on both simulated and real GNSS datasets show that the approach is effective, especially when applied before carrying out variance component estimation. Numéro de notice : A2017-709 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-017-1038-6 En ligne : https://doi.org/10.1007/s00190-017-1038-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88090 [article]

Reference satellite selection method for GNSS high-precision relative positioning / Xiao Gao in Geodesy and Geodynamics, vol 8 n° 2 (March 2017)  

Public [article]  inGeodesy and Geodynamics > vol 8 n° 2 (March 2017) . - pp 125 - 129 Titre : Reference satellite selection method for GNSS high-precision relative positioning Type de document : Article/Communication Auteurs : Xiao Gao, Auteur ; Wujiao Dai, Auteur ; Zhiyong Song, Auteur ; Changsheng Cai, Auteur Année de publication : 2017 Article en page(s) : pp 125 - 129 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] affaiblissement géométrique de la précision [Termes IGN] positionnement différentiel [Termes IGN] positionnement par GNSS Résumé : (auteur) Selecting the optimal reference satellite is an important component of high-precision relative positioning because the reference satellite directly influences the strength of the normal equation. The reference satellite selection methods based on elevation and positional dilution of precision (PDOP) value were compared. Results show that all the above methods cannot select the optimal reference satellite. We introduce condition number of the design matrix in the reference satellite selection method to improve structure of the normal equation, because condition number can indicate the ill condition of the normal equation. The experimental results show that the new method can improve positioning accuracy and reliability in precise relative positioning. Numéro de notice : A2017-236 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.geog.2016.07.007 En ligne : https://doi.org/10.1016/j.geog.2016.07.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85168 [article]