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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)
[article]
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 IGN] bruit atmosphérique
[Termes IGN] décalage d'horloge
[Termes IGN] erreur systématique interfréquence d'horloge
[Termes IGN] fréquence multiple
[Termes IGN] modèle mathématique
[Termes IGN] phase
[Termes IGN] positionnement ponctuel précis
[Termes IGN] signal BeiDou
[Termes IGN] signal Galileo
[Termes IGN] signal GLONASS
[Termes IGN] signal GNSS
[Termes IGN] signal GPS
[Termes IGN] temps-fréquence
[Termes IGN] transmission de donnéesRésumé : (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 Affiliation des auteurs : non IGN 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
in GPS solutions > vol 24 n° 2 (April 2020)[article]What, where, and how to transfer in SAR target recognition based on deep CNNs / Zhongling Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
[article]
Titre : What, where, and how to transfer in SAR target recognition based on deep CNNs Type de document : Article/Communication Auteurs : Zhongling Huang, Auteur ; Zongxu Pan, Auteur ; Bin Lei, Auteur Année de publication : 2020 Article en page(s) : pp 2324 - 2336 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de cible
[Termes IGN] données multisources
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] source de données
[Termes IGN] transmission de donnéesRésumé : (auteur) Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated data set in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in synthetic aperture radar (SAR) image interpretation. Transfer learning provides an effective way to solve this problem by borrowing knowledge from the source task to the target task. In optical remote sensing application, a prevalent mechanism is to fine-tune on an existing model pretrained with a large-scale natural image data set, such as ImageNet. However, this scheme does not achieve satisfactory performance for SAR applications because of the prominent discrepancy between SAR and optical images. In this article, we attempt to discuss three issues that are seldom studied before in detail: 1) what network and source tasks are better to transfer to SAR targets; 2) in which layer are transferred features more generic to SAR targets; and 3) how to transfer effectively to SAR targets recognition. Based on the analysis, a transitive transfer method via multisource data with domain adaptation is proposed in this article to decrease the discrepancy between the source data and SAR targets. Several experiments are conducted on OpenSARShip. The results indicate that the universal conclusions about transfer learning in natural images cannot be completely applied to SAR targets, and the analysis of what and where to transfer in SAR target recognition is helpful to decide how to transfer more effectively. Numéro de notice : A2020-195 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947634 Date de publication en ligne : 20/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947634 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94863
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2324 - 2336[article]A proposal for modeling indoor–outdoor spaces through indoorGML, open location code and OpenStreetMap / Ruben Cantarero Navarro in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)
[article]
Titre : A proposal for modeling indoor–outdoor spaces through indoorGML, open location code and OpenStreetMap Type de document : Article/Communication Auteurs : Ruben Cantarero Navarro, Auteur ; Ana Rubio Ruiz, Auteur ; Javier Dorado Chaparro, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 21 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] CityGML
[Termes IGN] données hétérogènes
[Termes IGN] format Industry foudation classes IFC
[Termes IGN] indoorGML
[Termes IGN] internet des objets
[Termes IGN] modélisation 3D
[Termes IGN] OpenStreetMap
[Termes IGN] positionnement en intérieur
[Termes IGN] représentation spatiale
[Termes IGN] ville intelligente
[Termes IGN] WiFiRésumé : (auteur) Traditionally, the standards of spatial modeling are oriented to represent the quantitative information of space. However, in recent years an increasingly common challenge is appearing: flexibly and appropriately integrating quantitative information that goes beyond the purely geometric. This problem has been aggravated due to the success of new paradigms such as the Internet of Things. This adds an additional challenge to the representation of this information due to the need to represent characteristic information of the space from different points of view in a model, such as WiFi coverage, dangerous surroundings, etc. While this problem has already been addressed in indoor spaces with the IndoorGML standard, it remains to be solved in outdoor and indoor–outdoor spaces. We propose to take the advantages proposed in IndoorGML, such as cellular space or multi-layered space model representation, to outdoor spaces in order to create indoor–outdoor models that enable the integration of heterogeneous information that represents different aspects of space. We also propose an approach that gives more flexibility in spatial representation through the integration of standards such as OpenLocationCode for the division of space. Further, we suggest a procedure to enrich the resulting model through the information available in OpenStreetMap. Numéro de notice : A2020-257 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9030169 Date de publication en ligne : 12/03/2020 En ligne : https://doi.org/10.3390/ijgi9030169 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95013
in ISPRS International journal of geo-information > vol 9 n° 3 (March 2020) . - 21 p.[article]
Titre : Distributed and parallel architectures for spatial data Type de document : Monographie Auteurs : Alberto Belussi, Éditeur scientifique ; Sara Migliorini, Éditeur scientifique ; Damiano Carra, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 170 p. ISBN/ISSN/EAN : 978-3-03936-751-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] base de données localisées
[Termes IGN] collecte de données
[Termes IGN] développement durable
[Termes IGN] données localisées
[Termes IGN] données massives
[Termes IGN] entrepôt de données localisées
[Termes IGN] géoportail
[Termes IGN] Hadoop
[Termes IGN] métadonnées
[Termes IGN] modèle numérique de surface
[Termes IGN] objet mobile
[Termes IGN] OLAP
[Termes IGN] OpenStreetMap
[Termes IGN] PostGIS
[Termes IGN] réseau social
[Termes IGN] SQL
[Termes IGN] système d'information géographique
[Termes IGN] téléphone intelligent
[Termes IGN] traitement parallèle
[Termes IGN] zone tamponRésumé : (Editeur) [Préface] In recent years, an increasing amount of spatial data has been collected by different types of devices, such as mobile phones, sensors, satellites, space telescope, and medical tools for analysis, or is generated by social networks, such as geotagged tweets. The processing of this huge amount of information, including spatial properties, which are frequently represented in heterogeneous ways, is a challenging task that has boosted research in the big data area in an attempt to investigate cases and propose new solutions for dealing with its peculiarities. In the literature, many different proposals and approaches for facing the problem have been proposed, addressing different goals and different types of users. However, most are obtained by customizing existing approaches which were originally developed for the processing of big data of the alphanumeric type, without any specific support for spatial or spatiotemporal properties. Thus, the proposed solutions can exploit the parallelism provided by these kinds of systems, but without taking into account, in a proficient way, the space and time dimensions that intrinsically characterize the analyzed datasets. As described in the literature, current solutions include: (i) the on-top approach, where an underlying system for traditional big datasets is used as a black box while spatial processing is added through the definition of user-defined functions that are specified on top of the underlying system; (ii) the from-scratch approach, where a completely new system is implemented for a specific application context; and (iii) the built-in approach, where an existing solution is extended by injecting spatial data functions into its core. This book aims at promoting new and innovative studies, proposing new architectures or innovative evolutions of existing ones, and illustrating experiments on current technologies in order to improve the efficiency and effectiveness of distributed and cluster systems when they deal with spatiotemporal data. Note de contenu : Preface
1- Distributed Processing of Location-Based Aggregate Queries Using MapReduce
2- Towards the Development of Agenda 2063 Geo-Portal to Support Sustainable Development in Africa
3- HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time
4- Mobility DataWarehouses
5- Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC
6- LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data
7- Mr4Soil: A MapReduce-Based Framework Integrated with GIS for Soil Erosion Modelling
8- High-Performance Geospatial Big Data Processing System Based on MapReduceNuméro de notice : 25884 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03936-751-1 En ligne : https://doi.org/10.3390/books978-3-03936-751-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95762 Evaluation des mesures GPS effectuées par un smartphone Android Xiaomi Mi 8 / Umberto Robustelli in Géomatique expert, n° 132-133 (janvier - septembre 2020)
[article]
Titre : Evaluation des mesures GPS effectuées par un smartphone Android Xiaomi Mi 8 Type de document : Article/Communication Auteurs : Umberto Robustelli, Auteur ; Valerio Baiocchi, Auteur ; Giovanni Pugliano, Auteur Année de publication : 2020 Article en page(s) : pp 38 - 49 Note générale : bibliographie
article original en anglais en libre accès : https://doi.org/10.3390/electronics8010091Langues : Français (fre) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] Androïd
[Termes IGN] précision du positionnement
[Termes IGN] récepteur bifréquence
[Termes IGN] téléphone intelligentRésumé : (auteur) [original abstract] On May 2018 the world’s first dual-frequency Global Navigation Satellite System (GNSS) smartphone produced by Xiaomi equipped with a Broadcom BCM47755 chip was launched. It is able to receive L1/E1/ and L5/E5 signals from GPS, Galileo, Beidou, and GLONASS (GLObal NAvigation Satellite System) satellites. The main aim of this work is to achieve the phone’s position by using multi-constellation, dual frequency pseudorange and carrier phase raw data collected from the smartphone. Furthermore, the availability of dual frequency raw data allows to assess the multipath performance of the device. The smartphone’s performance is compared with that of a geodetic receiver. The experiments were conducted in two different scenarios to test the smartphone under different multipath conditions. Smartphone measurements showed a lower C/N0 and higher multipath compared with those of the geodetic receiver. This produced negative effects on single-point positioning as showed by high root mean square error (RMS). The best positioning accuracy for single point was obtained with the E5 measurements with a DRMS (horizontal root mean square error) of 4.57 m. For E1/L1 frequency, the 2DRMS was 5.36 m. However, the Xiaomi Mi 8, thanks to the absence of the duty cycle, provided carrier phase measurements used for a static single frequency relative positioning with an achieved 2DRMS of 1.02 and 1.95 m in low and high multipath sites, respectively. Numéro de notice : A2020-862 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99184
in Géomatique expert > n° 132-133 (janvier - septembre 2020) . - pp 38 - 49[article]Réservation
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