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Performance of Galileo precise time and frequency transfer models using quad-frequency carrier phase observations / Pengfei Zhang in GPS solutions, vol 24 n° 2 (April 2020)  

Public [article]  inGPS solutions > vol 24 n° 2 (April 2020) Titre : Performance of Galileo precise time and frequency transfer models using quad-frequency carrier phase observations Type de document : Article/Communication Auteurs : Pengfei Zhang, Auteur ; Rui Tu, Auteur ; Yuping Gao, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] bruit atmosphérique [Termes descripteurs IGN] code Galileo [Termes descripteurs IGN] décalage d'horloge [Termes descripteurs IGN] erreur systématique interfréquence d'horloge [Termes descripteurs IGN] fréquence multiple [Termes descripteurs IGN] modèle mathématique [Termes descripteurs IGN] phase [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] signal BeiDou [Termes descripteurs IGN] signal Galileo [Termes descripteurs IGN] signal GLONASS [Termes descripteurs IGN] signal GNSS [Termes descripteurs IGN] signal GPS [Termes descripteurs IGN] temps-fréquence [Termes descripteurs IGN] transmission de données Résumé : (auteur) GNSSs, such as Galileo and modernized GPS, BeiDou and GLONASS systems, offer new potential and challenges in precise time and frequency transfer using multi-frequency observations. We focus on the performance of Galileo time and frequency transfer using the E1, E5a, E5b and E5 observations. Dual-frequency, triple-frequency and quad-frequency models for precise time and frequency transfer with different Galileo observations are proposed. Four time and transfer links between international time laboratories are used to assess the performances of different models in terms of time link noise level and frequency stability indicators. The average RMS values of the smoothed residuals of the clock difference series are 0.033 ns, 0.033 ns and 0.034 ns for the dual-frequency, triple-frequency and quad-frequency models with four time links, respectively. With respect to frequency stability, the average stability values at 15,360 s are 9.51 × 10−15, 9.46 × 10−15 and 9.37 × 10−15 for the dual-frequency, triple-frequency and quad-frequency models with four time links, respectively. Moreover, although biases among different models and receiver the inter-frequency exist, their characteristics are relatively stable. Generally, the dual-/triple-/quad-frequency models show similar performance for those time links, and the quad-frequency models can provide significant potential for switching among and unifying the three multi-frequency solutions, as well as further enhancing the redundancy and reliability compared to the current dual-frequency time transfer method. Numéro de notice : A2020-083 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-0955-7 date de publication en ligne : 04/02/2020 En ligne : https://doi.org/10.1007/s10291-020-0955-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94652 [article]

Deep SAR-Net: learning objects from signals / Zhongling Huang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 179 - 193 Titre : Deep SAR-Net: learning objects from signals Type de document : Article/Communication Auteurs : Zhongling Huang, Auteur ; Mihai Datcu, Auteur ; Zongxu Pan, Auteur ; Bin Lei, Auteur Année de publication : 2020 Article en page(s) : pp 179 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image Terra [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] micro-onde [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] temps-fréquence Résumé : (Auteur) This paper introduces a novel Synthetic Aperture Radar (SAR) specific deep learning framework for complex-valued SAR images. The conventional deep convolutional neural networks based methods usually take the amplitude information of single-polarization SAR images as the input to learn hierarchical spatial features automatically, which may have difficulties in discriminating objects with similar texture but discriminative scattering patterns. Our novel deep learning framework, Deep SAR-Net, takes complex-valued SAR images into consideration to learn both spatial texture information and backscattering patterns of objects on the ground. On the one hand, we transfer the detected SAR images pre-trained layers to extract spatial features from intensity images. On the other hand, we dig into the Fourier domain to learn physical properties of the objects by joint time-frequency analysis on complex-valued SAR images. We evaluate the effectiveness of Deep SAR-Net on three complex-valued SAR datasets from Sentinel-1 and TerraSAR-X satellite and demonstrate how it works better than conventional deep CNNs, especially on man-made objects classes. The proposed datasets and the trained Deep SAR-Net model with all codes are provided. Numéro de notice : A2020-065 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.016 date de publication en ligne : 23/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.016 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94583 [article]

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Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks / Angel Bueno in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp Titre : Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks Type de document : Article/Communication Auteurs : Angel Bueno, Auteur ; Carmen Benitez, Auteur ; Silvio De Angelis, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] forme d'onde [Termes descripteurs IGN] incertitude des données [Termes descripteurs IGN] réseau bayesien [Termes descripteurs IGN] réseau de neurones profond [Termes descripteurs IGN] Russie [Termes descripteurs IGN] séisme [Termes descripteurs IGN] sismologie [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] volcanologie [Termes descripteurs IGN] Washington (Etats-Unis ; état) Résumé : (auteur) Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL; we implement and demonstrate a workflow for waveform classification, rapid quantification of the associated uncertainty, and link these uncertainties to changes in volcanic unrest. Specifically, we introduce Bayesian neural networks (BNNs) to perform event identification, classification, and their estimated uncertainty on data gathered at two active volcanoes, Mount St. Helens, Washington, USA, and Bezymianny, Kamchatka, Russia. We demonstrate how BNNs achieve excellent performance (92.08%) in discriminating both the type of event and its origin when the two data sets are merged together, and no additional training information is provided. Finally, we demonstrate that the data representations learned by the BNNs are transferable across different eruptive periods. We also find that the estimated uncertainty is related to changes in the state of unrest at the volcanoes and propose that it could be used to gauge whether the learned models may be exported to other eruptive scenarios. Numéro de notice : A2020-094 Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2941494 date de publication en ligne : 07/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2941494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94657 [article]

Assessing the quality of ionospheric models through GNSS positioning error: methodology and results / Adria Rovira-Garcia in GPS solutions, vol 24 n° 1 (January 2020)  

Public [article]  inGPS solutions > vol 24 n° 1 (January 2020) Titre : Assessing the quality of ionospheric models through GNSS positioning error: methodology and results Type de document : Article/Communication Auteurs : Adria Rovira-Garcia, Auteur ; Deimos Ibáñez-Segura, Auteur ; Raül Orús-Pérez, Auteur ; et al., Auteur Année de publication : 2020 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] erreur de positionnement [Termes descripteurs IGN] International GNSS Service [Termes descripteurs IGN] modèle ionosphérique [Termes descripteurs IGN] phase [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] retard ionosphèrique [Termes descripteurs IGN] trajet multiple [Termes descripteurs IGN] valeur aberrante Résumé : (Auteur) Single-frequency users of the global navigation satellite system (GNSS) must correct for the ionospheric delay. These corrections are available from global ionospheric models (GIMs). Therefore, the accuracy of the GIM is important because the unmodeled or incorrectly part of ionospheric delay contributes to the positioning error of GNSS-based positioning. However, the positioning error of receivers located at known coordinates can be used to infer the accuracy of GIMs in a simple manner. This is why assessment of GIMs by means of the position domain is often used as an alternative to assessments in the ionospheric delay domain. The latter method requires accurate reference ionospheric values obtained from a network solution and complex geodetic modeling. However, evaluations using the positioning error method present several difficulties, as evidenced in recent works, that can lead to inconsistent results compared to the tests using the ionospheric delay domain. We analyze the reasons why such inconsistencies occur, applying both methodologies. We have computed the position of 34 permanent stations for the entire year of 2014 within the last Solar Maximum. The positioning tests have been done using code pseudoranges and carrier-phase leveled (CCL) measurements. We identify the error sources that make it difficult to distinguish the part of the positioning error that is attributable to the ionospheric correction: the measurement noise, pseudorange multipath, evaluation metric, and outliers. Once these error sources are considered, we obtain equivalent results to those found in the ionospheric delay domain assessments. Accurate GIMs can provide single-frequency navigation positioning at the decimeter level using CCL measurements and better positions than those obtained using the dual-frequency ionospheric-free combination of pseudoranges. Finally, some recommendations are provided for further studies of ionospheric models using the position domain method. Numéro de notice : A2020-024 Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-019-0918-z date de publication en ligne : 02/11/2019 En ligne : https://doi.org/10.1007/s10291-019-0918-z Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94468 [article]

Reducing convergence time of precise point positioning with ionospheric constraints and receiver differential code bias modeling / Yan Xiang in Journal of geodesy, vol 94 n°1 (January 2020)  

Public [article]  inJournal of geodesy > vol 94 n°1 (January 2020) Titre : Reducing convergence time of precise point positioning with ionospheric constraints and receiver differential code bias modeling Type de document : Article/Communication Auteurs : Yan Xiang, Auteur ; Yang Gao, Auteur ; Yihe Li, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes descripteurs IGN] carte ionosphérique mondiale [Termes descripteurs IGN] double différence [Termes descripteurs IGN] horloge du récepteur [Termes descripteurs IGN] mesurage de phase [Termes descripteurs IGN] modèle ionosphérique [Termes descripteurs IGN] phase [Termes descripteurs IGN] positionnement différentiel [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] retard ionosphèrique [Termes descripteurs IGN] temps de convergence [Termes descripteurs IGN] teneur totale en électrons Résumé : (auteur) Long convergence time has limited the wide application of traditional precise point positioning (PPP) based on an ionosphere-free combination of dual-frequency observations. Different from the traditional PPP, the uncombined PPP method based on raw observations estimates ionospheric delays. When external ionospheric information is available, it can be applied as a constraint to help shorten the convergence time, as a result of the reduced correlation between the position and the ionospheric parameters. The receiver differential code biases (DCBs) will be a concern, however, when applying the external ionospheric information. For receiver DCBs, it is usually assumed that the biases can be absorbed by the receiver clock parameters. We have demonstrated that the receiver DCBs cannot be fully assimilated by one receiver code clock parameter because the receiver DCBs have different effects on the code and carrier phase measurements at any frequency. Additional parameters are necessary to model the receiver DCBs so that their effects on the positioning solution can be minimized. We developed an ionosphere-constrained PPP model to incorporate ionospheric total electron content (TEC) in the slant (STEC) and vertical (VTEC) when leveraging a regional network and global ionospheric maps (GIMs). Both static and kinematic experimental results show that the convergence time and the positioning accuracy can be improved significantly. Accuracies at the first epoch of 0.4 m for GIM constraints, and 0.2 m for the regional constraints, are achievable. The convergence time to 1 dm horizontal accuracy is reduced to 7.5 min at a 68% confidence level. Numéro de notice : A2020-149 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-019-01334-x date de publication en ligne : 02/01/2020 En ligne : https://doi.org/10.1007/s00190-019-01334-x Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94781 [article]

Soil and vegetation scattering contributions in L-Band and P-Band polarimetric SAR observations / S. Hamed Alemohammad in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

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Performance of Galileo-only dual-frequency absolute positioning using the fully serviceable Galileo constellation / Tomasz Hadas in GPS solutions, vol 23 n° 4 (October 2019)  

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An analytic expression for the phase noise of the goldstein–werner filter / Scott Hensley in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)  

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Mapping the wavelength position of mineral features in hyperspectral thermal infrared data / Christoph Hecker in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)  

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An improved robust Kalman filtering strategy for GNSS kinematic positioning considering small cycle slips / Wanke Liu in Advances in space research, vol 63 n° 9 (1 May 2019)  

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Albedo estimation for real-time 3D reconstruction using RGB-D and IR data / Patrick Stotko in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)  

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A new waveform decomposition method for multispectral LiDAR / Shalei Song in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)  

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Monitoring crops water needs at high spatio-temporal resolution by synergy of optical / thermal and radar observations / Abdelhakim Amazirh (2019)  

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Real-time capturing of seismic waveforms using high-rate BDS, GPS and GLONASS observations: the 2017 Mw 6.5 Jiuzhaigou earthquake in China / Xingxing Li in GPS solutions, vol 23 n° 1 (January 2019)  

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Analyzing the role of pulse density and voxelization parameters on full-waveform LiDAR-derived metrics / Pablo Crespo-Peremarch in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

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