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A determination of the motion based on GNSS observations between 2000 and 2021 using the IGS points in the polar regions / Atinç Pirti in Geodesy and cartography, vol 48 n° 3 (October 2022)
[article]
Titre : A determination of the motion based on GNSS observations between 2000 and 2021 using the IGS points in the polar regions Type de document : Article/Communication Auteurs : Atinç Pirti, Auteur Année de publication : 2022 Article en page(s) : pp 177 - 184 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse diachronique
[Termes IGN] Antarctique
[Termes IGN] Arctique
[Termes IGN] données GNSS
[Termes IGN] international GPS service for geodynamics
[Termes IGN] International Terrestrial Reference Frame
[Termes IGN] mouvement du pôle
[Termes IGN] réseau géodésique
[Termes IGN] série temporelle
[Vedettes matières IGN] Traitement de données GNSSRésumé : (auteur) People are fascinated today more than ever by the polar regions of the Earth. One reason for this is that wide expanses of the Arctic and Antarctic have not been explored and are therefore still viewed as frontier regions. Another is that they both have very diverse histories with regard to their origins and ice formation. Their numerous aspects still pose many puzzles for science today. The regions of the Earth designated as polar are those areas located between the North or South Pole and the Arctic or Antarctic Circles, respectively. The northern polar region, called the Arctic, encompasses the Arctic Ocean and a portion of some surrounding land masses. The southern polar region, called the Antarctic, contains the continent of Antarctica and areas of the surrounding Southern Ocean. In this paper three tests (2000, 2010 and 2021) of continuous GNSS data recorded by 8 permanent International GPS Service (IGS) stations in both Polar Regions have been processed by using CSRS-PPP Software for geodetic networks. The results also show that all GNSS provide good visibility with low elevation angles, whereas with high elevation angles, which might be needed due to natural barriers, the GLONASS and other satellites provides the highest number of visible satellites. Consequently, the mean motion of the study area was found approximately 7–15 cm for horizontal components (X–Y) and 6 cm for vertical components (Ellipsoidal Height) on the eight IGS points in the both poles. Numéro de notice : A2022-761 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3846/gac.2022.14848 Date de publication en ligne : 02/09/2022 En ligne : https://doi.org/10.3846/gac.2022.14848 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101777
in Geodesy and cartography > vol 48 n° 3 (October 2022) . - pp 177 - 184[article]GNSS best integer equivariant estimation combining with integer least squares estimation: an integrated ambiguity resolution method with optimal integer aperture test / Liye Ma in GPS solutions, vol 26 n° 4 (October 2022)
[article]
Titre : GNSS best integer equivariant estimation combining with integer least squares estimation: an integrated ambiguity resolution method with optimal integer aperture test Type de document : Article/Communication Auteurs : Liye Ma, Auteur ; Yidong Lou, Auteur ; Liguo Lu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] analyse comparative
[Termes IGN] méthode des moindres carrés
[Termes IGN] phase GNSS
[Termes IGN] positionnement par GNSS
[Termes IGN] précision du positionnement
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) Accurate and reliable carrier phase ambiguity resolution (AR) is the key to global navigation satellite system (GNSS) high-precision navigation and positioning applications. The integer least squares (ILS) estimation and the best integer equivariant (BIE) estimation are two widely used AR method, with the former considered to have the highest success rate and the latter to be optimal in the minimum mean squared error (MSE) sense. We analyzed three key issues of applying the BIE method in detail, including the use boundary of BIE, the number of candidates to be involved, and the weight determination among ambiguity candidates. It has been demonstrated that the BIE estimator is superior to ILS estimator from an overall perspective, but not always the best in each specific epoch. Therefore, we recommend constructing an integrated ambiguity resolution scheme that combines BIE with ILS, and we propose to adopt the optimal integer aperture (OIA) test as a criterion to distinguish the two. Moreover, a new criterion referred to the OIA test is proposed to determine the number of candidates involved in the BIE estimator. We also attempt to add the quadratic forms of baseline residuals into the weight function of BIE, aiming to reach a more accurate estimator. Finally, an integrated AR method that combines ILS with BIE and distinguished by the OIA test is proposed, named OIA-BIE. A set of real-measured vehicle data are tested to evaluate its performance, compared to least squares (LS), ILS, and the original BIE. The results show that the positioning accuracy of OIA-BIE is a little better than BIE, better than ILS, and significantly better than LS. Moreover, the average time consumption of ILS, BIE, and OIA-BIE are also evaluated, with 1.15, 14.62, and 3.71 ms, respectively, and OIA-BIE is four times more efficient than BIE. Numéro de notice : A2022-542 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01285-5 Date de publication en ligne : 03/07/2022 En ligne : https://doi.org/10.1007/s10291-022-01285-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101107
in GPS solutions > vol 26 n° 4 (October 2022) . - n° 100[article]Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity / Omid Memarian Sorkhabi in Earth and space science, vol 9 n° 10 (October 2022)
[article]
Titre : Investigating the efficiency of deep learning methods in estimating GPS geodetic velocity Type de document : Article/Communication Auteurs : Omid Memarian Sorkhabi, Auteur ; Muhammed Milani, Auteur ; Seyed Mehdi Seyed Alizadeh, Auteur Année de publication : 2022 Article en page(s) : 8 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] apprentissage profond
[Termes IGN] champ de vitesse
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] géodynamique
[Termes IGN] point géodésique
[Termes IGN] positionnement par GPS
[Termes IGN] station GPS
[Termes IGN] tectoniqueRésumé : (auteur) Geodetic velocity (GV) has many applications in tectonic motion determination and geodynamic studies. Due to the high cost of global navigation satellite system stations, deep learning methods have been investigated to estimate GV. In this research, four methods of convolutional neural networks (CNNs), deep Boltzmann machines, deep belief net and recurrent neural networks have been applied. The GV of 42 global positioning system stations is entered the deep learning methods. The outputs of the four methods have successfully passed the normality test. The results show that the CNN method has a lower goodness of fit and root mean square error (RMSE). CNN can learn different dependencies and extract features. Numéro de notice : A2022-757 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1029/2021EA002202 Date de publication en ligne : 22/09/2022 En ligne : https://doi.org/10.1029/2021EA002202 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101763
in Earth and space science > vol 9 n° 10 (October 2022) . - 8 p.[article]Modelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches / Wenzong Gao in Journal of geodesy, vol 96 n° 10 (October 2022)
[article]
Titre : Modelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches Type de document : Article/Communication Auteurs : Wenzong Gao, Auteur ; Zhao Li, Auteur ; Qusen Chen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] déformation de la croute terrestre
[Termes IGN] données GNSS
[Termes IGN] Extreme Gradient Machine
[Termes IGN] modèle de simulation
[Termes IGN] série temporelle
[Vedettes matières IGN] Traitement de données GNSSRésumé : (auteur) Global navigation satellite system (GNSS) site coordinate time series provides essential data for geodynamic and geophysical studies, realisation of a regional or global geodetic reference frames, and crustal deformation research. The coordinate time series has been conventionally modelled by least squares (LS) fitting with harmonic functions, alongside many other analysis methods. As a key limitation, the traditional modelling approaches simply use the functions of time variable, despite good knowledge of various underlying physical mechanisms responsible for the site displacements. This paper examines the use of machine learning (ML) models to reflect the effects or residential effects of physical variables related to Sun and the Moon ephemerides, polar motion, temperature, atmospheric pressure, and hydrology on the site displacements. To form the ML problem, these variables are constructed as the input vector of each ML training sample, while the vertical displacement of a GNSS site is regarded as the output value. In the evaluation experiments, three ML approaches, namely the gradient boosting decision tree (GBDT) approach, long short-term memory (LSTM) approach, and support vector machine (SVM) approach, are introduced and evaluated with the time series datasets collected from 9 GNSS sites over the period of 13 years. The results indicate that all three approaches achieve similar fitting precision in the range of 3–5 mm in the vertical displacement component, which is an improvement in over 30% with respect to the traditional LS fitting precision in the range of 4–7 mm. The prediction of the vertical time series with the three ML approaches shows the precision in the range of 4–7 mm over the future 24- month period. The results also indicate the relative importance of different physical features causing the displacements of each site. Overall, ML approaches demonstrate better performance and effectiveness in modelling and prediction of GNSS time series, thus impacting maintenance of geodetic reference frames, geodynamics, geophysics, and crustal deformation analysis. Numéro de notice : A2022-737 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01662-5 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.1007/s00190-022-01662-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101709
in Journal of geodesy > vol 96 n° 10 (October 2022) . - n° 71[article]Multi‑constellation GNSS interferometric reflectometry for the correction of long-term snow height retrieval on sloping topography / Wei Zhou in GPS solutions, vol 26 n° 4 (October 2022)
[article]
Titre : Multi‑constellation GNSS interferometric reflectometry for the correction of long-term snow height retrieval on sloping topography Type de document : Article/Communication Auteurs : Wei Zhou, Auteur ; Liangke Huang, Auteur ; Bing Ji, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 140 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] hauteur (coordonnée)
[Termes IGN] manteau neigeux
[Termes IGN] pente
[Termes IGN] Ransac (algorithme)
[Termes IGN] rapport signal sur bruit
[Termes IGN] réflectométrie par GNSS
[Termes IGN] signal GNSS
[Termes IGN] système de référence altimétrique
[Termes IGN] topographie locale
[Termes IGN] transformation en ondelettes
[Termes IGN] valeur aberrante
[Vedettes matières IGN] AltimétrieRésumé : (auteur) Snow is a key parameter for global climate and hydrological systems. Global Navigation Satellite System interferometric reflectometry (GNSS-IR) has been applied to accurately monitor snow height (SH) with low cost and high temporal–spatial resolution. We proposed an improved GNSS-IR method using detrended signal-to-noise ratio (δSNR) arcs corresponding to multipath reflection tracks with different azimuths. After using wavelet decomposition and random sample consensus, noise with various frequencies for SNR arcs and outliers of reflector height (RH) estimations have been sequentially mitigated to enhance the availability of the proposed method. Thus, a height datum based on the ground RHs retrieved from multi-GNSS SNR data is established to compensate for the influence of topography variation with different azimuths in SH retrieval. The approximately 3-month δSNR datasets collected from three stations deployed on sloping topography were used to retrieve SH and compared with the existing method and in situ measurements. The results show that the root mean square errors of the retrievals derived from the proposed method for the three sites are between 4 and 8 cm, and the corresponding correlation surpasses 0.95 when compared to the reference SH datasets. Additionally, we compare the performance of a retrieval with the existing GNSS-IR Web App, and it shows an improvement in RMSE of about 7 cm. Furthermore, because topography variation has been considered, the average correction of SH retrievals is between 2 and 4 cm. The solution with the proposed method helps develop the applications of the GNSS-IR technique on complex topography. Numéro de notice : A2022-712 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-022-01333-0 Date de publication en ligne : 15/09/2022 En ligne : https://doi.org/10.1007/s10291-022-01333-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101590
in GPS solutions > vol 26 n° 4 (October 2022) . - n° 140[article]PPP rapid ambiguity resolution using Android GNSS raw measurements with a low-cost helical antenna / Xingxing Li in Journal of geodesy, vol 96 n° 10 (October 2022)PermalinkPrecise onboard time synchronization for LEO satellites / Florian Kunzi in Navigation : journal of the Institute of navigation, vol 69 n° 3 (Fall 2022)PermalinkSpherical harmonic synthesis of area-mean potential values on irregular surfaces / Blažej Bucha in Journal of geodesy, vol 96 n° 10 (October 2022)PermalinkThe use of gravity data to determine orthometric heights at the Hong Kong territories / Albertini Nsiah Ababio in Journal of applied geodesy, vol 16 n° 4 (October 2022)PermalinkToward BDS/Galileo/GPS/QZSS triple-frequency PPP instantaneous integer ambiguity resolutions without atmosphere corrections / Jun Tao in GPS solutions, vol 26 n° 4 (October 2022)PermalinkComparing Landsat-8 and Sentinel-2 top of atmosphere and surface reflectance in high latitude regions: case study in Alaska / Jiang Chen in Geocarto international, vol 37 n° 20 ([20/09/2022])PermalinkDense mantle flows periodically spaced below ocean basins / Isabelle Panet in Earth and planetary science letters, vol 594 (15 September 2022)PermalinkEstimation of swell height using spaceborne GNSS-R data from eight CYGNSS satellites / Yanli Zheng in Remote sensing, vol 14 n° 18 (September-2 2022)PermalinkAccuracy of GNSS RTK/NRTK height difference measurement / Robert Krzyzek in Applied geomatics, vol 14 n° 3 (September 2022)PermalinkAdaptive block modeling of time dependent variations of datum reference points in a tectonically active area / Chun-Yun Chou in Survey review, vol 54 n° 386 (September 2022)Permalink