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A multi-layer perceptron neural network to mitigate the interference of time synchronization attacks in stationary GPS receivers / N. Orouji in GPS solutions, vol 25 n° 3 (July 2021)  

Public [article]  inGPS solutions > vol 25 n° 3 (July 2021) . - Article 84 Titre : A multi-layer perceptron neural network to mitigate the interference of time synchronization attacks in stationary GPS receivers Type de document : Article/Communication Auteurs : N. Orouji, Auteur ; M. R. Mosavi, Auteur Année de publication : 2021 Article en page(s) : Article 84 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] décalage d'horloge [Termes IGN] horloge du récepteur [Termes IGN] méthode robuste [Termes IGN] Perceptron multicouche [Termes IGN] précision des données [Termes IGN] récepteur GPS [Termes IGN] station GPS [Termes IGN] synchronisation Résumé : (Auteur) Accurate timing is one of the key features of the Global Positioning System (GPS), which is employed in many critical infrastructures. Any imprecise time measurement in GPS-based structures, such as smart power grids, economic activities, and communication towers, can lead to disastrous results. The vulnerability of the stationary GPS receivers to the time synchronization attacks (TSAs) jeopardizes the GPS timing precision and trust level. In the past few years, studies suggested the adoption of estimators to follow the authentic trend of the clock offset information under attack conditions. However, the estimators would lose track of the authentic signal without proper knowledge of the signal characteristics. Therefore, a multi-layer perceptron neural network (MLP NN) is proposed to follow the trend of the data. The main difference between the proposed method and typical estimators is the reliance of the network on the training information consisting of signal features. The proposed MLP NN performance has been evaluated through two real-world datasets and two well-known types of TSA. The root mean square error results exhibit an improvement of at least six times compared to other conventional and state-of-art methods. Numéro de notice : A2021-331 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01124-z date de publication en ligne : 05/04/2021 En ligne : https://doi.org/10.1007/s10291-021-01124-z Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97501 [article]

Refining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data / Jia He in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3682 - 3694 Titre : Refining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data Type de document : Article/Communication Auteurs : Jia He, Auteur ; Zhizhao Liu, Auteur Année de publication : 2021 Article en page(s) : pp 3682 - 3694 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Amérique du nord [Termes IGN] données météorologiques [Termes IGN] données spatiotemporelles [Termes IGN] image Terra-MODIS [Termes IGN] modèle de régression [Termes IGN] modèle de transfert radiatif [Termes IGN] précision des données [Termes IGN] station GPS [Termes IGN] vapeur d'eau Résumé : (Auteur) A new algorithm of retrieving atmospheric water vapor from MODIS near-infrared (IR) (NIR) data by using a regression fitting method based on Global Positioning System (GPS)-derived water vapor is developed in this work. The algorithm has been used to retrieve total column water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) satellites both Terra and Aqua under cloud-free conditions from solar radiation in the NIR channels. Water vapor data estimated from GPS observations recorded from 2003 to 2017 by the SuomiNet GPS network over the western North America are used as ground truth references. The GPS stations were classified into six subsets based on the surface types adopted from MCD12Q1 IGBP legend. The differences in surface types are considered in the regression fitting procedure, thus different regression functions are trained for different surface types. Thus, the wet bias in the operational MODIS water vapor products has been significantly reduced. Water vapor retrieved from each of the three absorption channels and the weighted water vapor of combined three absorption channels are analyzed. Validation shows that the weighted water vapor performs better than the single-channel results. Compared to the MODIS/Terra water vapor products, the RMSE has been reduced by 50.78% to 2.229 mm using the two-channel ratio transmittance method and has been reduced by 53.06% to 2.126 mm using the three-channel ratio transmittance method. Compared to the MODIS/Aqua water vapor products, the RMSE has been reduced by 45.54% to 2.423 mm using the two-channel ratio transmittance method and has been reduced by 45.34% to 2.432 mm using the three-channel ratio transmittance method. Numéro de notice : A2021-338 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3016655 date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3016655 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97569 [article]

Determination of precise Galileo orbits using combined GNSS and SLR observations / Grzegorz Bury in GPS solutions, vol 25 n° 1 (January 2021)  

Public [article]  inGPS solutions > vol 25 n° 1 (January 2021) . - n° 11 Titre : Determination of precise Galileo orbits using combined GNSS and SLR observations Type de document : Article/Communication Auteurs : Grzegorz Bury, Auteur ; Krzysztof Sosnica, Auteur ; Radoslaw Zajdel, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 11 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Techniques orbitales [Termes IGN] données GNSS [Termes IGN] données TLS (télémétrie) [Termes IGN] Galileo [Termes IGN] International Terrestrial Reference Frame [Termes IGN] orbite précise [Termes IGN] orbitographie [Termes IGN] pondération Résumé : (auteur) Galileo satellites are equipped with laser retroreflector arrays for satellite laser ranging (SLR). In this study, we develop a methodology for the GNSS-SLR combination at the normal equation level with three different weighting strategies and evaluate the impact of laser observations on the determined Galileo orbits. We provide the optimum weighting scheme for precise orbit determination employing the co-location onboard Galileo. The combined GNSS-SLR solution diminishes the semimajor axis formal error by up to 62%, as well as reduces the dependency between values of formal errors and the elevation of the Sun above the orbital plane—the β angle. In the combined solution, the standard deviation of the SLR residuals decreases from 36.1 to 29.6 mm for Galileo-IOV satellites and |β|> 60°, when compared to GNSS-only solutions. Moreover, the bias of the Length-of-Day parameter is 20% lower for the combined solution when compared to the microwave one. As a result, the combination of GNSS and SLR observations provides promising results for future co-locations onboard the Galileo satellites for the orbit determination, realization of the terrestrial reference frames, and deriving geodetic parameters. Numéro de notice : A2021-008 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01045-3 date de publication en ligne : 31/10/2020 En ligne : https://doi.org/10.1007/s10291-020-01045-3 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96298 [article]

Galileo High Accuracy Service (HAS) / European GNSS Agency (GSA) (2021)  

Public Titre : Galileo High Accuracy Service (HAS) Type de document : Ouvrage divers Auteurs : European GNSS Agency (GSA), Auteur Editeur : Prague [République tchèque] : European GNSS Agency (GSA) Année de publication : 2021 Importance : 16 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] Galileo [Termes IGN] positionnement par Galileo [Termes IGN] positionnement ponctuel précis [Termes IGN] précision du positionnement Résumé : (auteur) With the Galileo High Accuracy Service (HAS), Galileo will pioneer a worldwide, free high-accuracy positioning service aimed at applications that require higher performance than that offered by the Open Service. The HAS will be based on the provision of PPP corrections (orbit, clock, biases, atmospheric corrections), at a maximum rate of 448 bps per Galileo satellite connected to an uplink station, allowing the user to obtain a horizontal positioning error of less than two decimetres (95 %) in nominal conditions of use. The parameters and the business model of the service will foster innovation in both consolidated and emerging markets, notably in key areas such as drones and autonomous cars, minimising disruption in the current business models of established providers. The HAS will be implemented in three phases, including testing and experimentation; an initial Galileo High Accuracy Service for a reduced number of signals, with relaxed performance targets and a reduced coverage area; and a full Galileo HAS that will offer two service levels and global coverage. Service Level 1 will provide PPP corrections globally to achieve 20 cm horizontal precision with Note de contenu : 1- Introduction 2- Target markets 3- HAS service characterisation 4- HAS high level architecture 5- HAS roadmap 6- HAS relevant documentation and interfaces avialable 7- Summary Numéro de notice : 28526 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Plaquette / brochure DOI : sans En ligne : https://www.gsa.europa.eu/newsroom/news/gsa-publishes-high-accuracy-service-info [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97339

Remote sensing and GIS / Basudeb Bhatta (2021)

Public Titre : Remote sensing and GIS Type de document : Guide/Manuel Auteurs : Basudeb Bhatta, Auteur Mention d'édition : 3ème édition Editeur : Oxford, Londres, ... : Oxford University Press Année de publication : 2021 Importance : 752 p. Format : 24 x 18 cm ISBN/ISSN/EAN : 978-0-19-949664-8 Note générale : Bibliographie additional reading material with Oxford areal Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] acquisition d'images [Termes IGN] airborne multispectral scanner [Termes IGN] analyse spatiale [Termes IGN] Global Navigation Satellite System [Termes IGN] image hyperspectrale [Termes IGN] image thermique [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] Lidar [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de terrain [Termes IGN] modélisation 3D [Termes IGN] orthorectification [Termes IGN] Passive and Active L and S band Sensor [Termes IGN] photographie aérienne [Termes IGN] Satellite Microwave Radiometer [Termes IGN] scène 3D [Termes IGN] stéréoscopie [Termes IGN] système d'information géographique [Termes IGN] traitement d'image [Termes IGN] visualisation 3D Index. décimale : 35.00 Télédétection - généralités Résumé : (Editeur) Beginning with the history and basic concepts of remote sensing and GIS, the book gives an exhaustive coverage of optical, thermal, and microwave remote sensing, global navigation satellite systems (such as GPS and IRNSS), digital photogrammetry, visual image analysis, digital image processing, spatial and attribute data model, geospatial analysis, and planning, implementation, and management of GIS. It also presents the modern trends of remote sensing and GIS with an illustrated discussion on its numerous applications. Note de contenu : 1. Concept of Remote Sensing 1.1 Introduction 1.2 Distance of Remote Sensing 1.3 Definition of Remote Sensing 1.4 Remote Sensing: Art and/or Science 1.5 Data 1.6 Remote Sensing Process 1.7 Source of Energy 1.8 Interaction with Atmosphere 1.9 Interaction with Target 1.9.1 Hemispherical Absorptance, Transmittance, and Reflectan 1.10 Interaction with the Atmosphere Again 1.11 Recording of Energy by Sensor 1.12 Transmission, Reception, and Processing 1.13 Interpretation and Analysis 1.14 Applications of Remote Sensing 1.15 Advantages of Remote Sensing 1.16 Limitations of Remote Sensing 1.17 Ideal Remote Sensing System 2. Types of Remote Sensing and Sensor Characteristics 2.1 Introduction 2.2 Types of Remote Sensing 2.3 Characteristics of Images 2.4 Orbital Characteristics of Satellite 2.5 Remote Sensing Satellites 2.6 Concept of Swath 2.7 Concept of Nadir 2.8 Sensor Resolutions 2.9 Image Referencing System 2.9.1 Path 2.9.2 Row 2.9.3 Orbital Calendar 3. History of Remote Sensing and Indian Space Program 3.1 Introduction 3.2 The Early Age 3.3 The Middle Age 3.4 The Modern Age or Space Age 3.5 Indian Space Program 4. Photographic Imaging 4.1 Introduction 4.2 Camera Systems 4.3 Types of Camera 4.4 Filter 4.5 Film 4.6 Geometry of Aerial Photography 4.7 Ideal Time and Atmosphere for Aerial Remote Sensing 5. Digital Imaging 5.1 Introduction 5.2 Digital Image 5.3 Sensor 5.4 Imaging by Scanning Technique 5.5 Hyper-spectral Imaging 5.6 Imaging By Non-scanning Technique 5.7 Thermal Remote Sensing 5.8 Other Sensors 6. Microwave Remote Sensing 6.1 Introduction 6.2 Passive Microwave Remote Sensing 6.3 Active Microwave Remote Sensing 6.4 Radar Imaging 6.5 Airborne Versus Space-Borne Radars 6.6 Radar Systems 7. Ground-truth Data and Global Positioning System 7.1 Introduction 7.2 Requirements of Ground-Truth Data 7.3 Instruments for Ground Truthing 7.4 Parameters of Ground Truthing 7.5 Factors of Spectral Measurement 7.6 Global Navigation Satellite System 8. Photogrammetry 8.1 Introduction 8.2 Development of Photogrammetry 8.3 Classification of Photogrammetry 8.4 Photogrammetric Process 8.5 Acquisition of Imagery and its Support Data 8.6 Orientation and Triangulation 8.7 Stereo Model Compilation 8.8 Stereoscopic 3D Viewing 8.9 Stereoscopic Measurement 8.10 DTM/DEM Generation 8.11 Contour Map Generation 8.12 Orthorectification 8.13 3D Feature Extraction 8.14 3D Scene Modelling 8.15 Photogrammetry and LiDAR 8.16 Radargrammetry and Radar Interferometry 8.17 Limitations of Photogrammetry 9. Visual Image Interpretation 9.1 Introduction 9.2 Information Extraction by Human and Computer 9.3 Remote Sensing Data Products 9.4 Border or Marginal Information 9.5 Image Interpretation 9.6 Elements of Visual Image Interpretation 9.7 Interpretation Keys 9.8 Generation of Thematic Maps 9.9 Thermal Image Interpretation 9.10 Radar Image Interpretation 10. Digital Image Processing 10.1 Introduction 10.2 Categorization of Image Processing 10.3 Image Processing Systems 10.4 Digital Image 10.5 Media for Digital Data Recording, Storage, and Distribution 10.6 Data Formats of Digital Image 10.7 Header Information 10.8 Display of Digital Image 10.9 Pre-processing 10.10 Image Enhancement 10.11 Image Transformation 10.12 Image Classification 11. Data Integration, Analysis, and Presentation 11.1 Introduction 11.2 Multi-approach of Remote Sensing 11.3 Integration with Ground Truth and Other Ancillary Data 11.4 Integration of Transformed Data 11.5 Integration with GIS 11.6 Process of Remote Sensing Data Analysis 11.7 The Level of Detail 11.8 Limitations of Remote Sensing Data Analysis 11.9 Presentation 12. Applications of Remote Sensing 12.1 Introduction 12.2 Land Cover and Land Use 12.3 Agriculture 12.4 Forestry 12.5 Geology 12.6 Geomorphology 12.7 Urban Applications 12.8 Hydrology 12.9 Mapping 12.10 Oceans and Coastal Monitoring 12.11 Monitoring of Atmospheric Constituents PART II Geographic Information Systems and Geospatial Analysis 13. Concept of Geographic Information Systems 13.1 Introduction 13.2 Definitions of GIS 13.3 Key Components of GIS 13.4 GIS-An Integration of Spatial and Attribute Information 13.5 GIS-Three Views of Information System 13.6 GIS and Related Terms 13.7 GIS-A Knowledge Hub 13.8 GIS-A Set of Interrelated Subsystems 13.9 GIS-An Information Infrastructure 13.10 Origin of GIS 14. Functions and Advantages of GIS 14.1 Introduction 14.2 Functions of GIS 14.3 Application Areas of GIS 14.4 Advantages of GIS 14.5 Functional Requirements of GIS 14.6 Limitations of GIS 15. Spatial Data Model 15.1 Introduction 15.2 Spatial, Thematic, and Temporal Dimensions of Geographic Data 15.3 Spatial Entity and Object 15.4 Spatial Data Model 15.5 Raster Data Model 15.6 Vector Data Model 15.7 Raster versus Vector 15.8 Object-Oriented Data Model 15.9 File Formats of Spatial Data 16. Attribute Data Management and Metadata Concept 16.1 Introduction 16.2 Concept of Database and DBMS 16.3 Advantages of DBMS 16.4 Functions of DBMS 16.5 File and Data Access 16.6 Data Models 16.7 Database Models 16.8 Data Models in GIS 16.9 Concept of SQL 16.10 Concept of Metadata 17. Process of GIS 17.1 Introduction 17.2 Data Capture 17.3 Data Sources 17.4 Data Encoding Methods 17.5 Linking of Spatial and Attribute Data 17.6 Organizing Data for Analysis 18. Geospatial Analysis 18.1 Introduction 18.2 Geospatial Data Analysis 18.3 Integration and Modelling of Spatial Data 18.4 Geospatial Data Analysis Methods 18.5 Database Query 18.6 Geospatial Measurements 18.7 Overlay Operations 18.8 Network Analysis 18.9 Surface Analysis 18.10 Geostatistics 18.11 Geovisualization 19. Planning, Implementation, and Management of GIS 19.1 Introduction 19.2 Planning of Project 19.3 Implementation of Project 19.4 Management of Project 19.5 Keys for Successful GIS 19.6 Reasons for Unsuccessful GIS 20. Modern Trends of GIS 20.1 Introduction 20.2 Local to Global Concept in GIS 20.3 Increase in Dimensions in GIS 20.4 Linear to Non-linear Techniques in GIS 20.5 Development in Relation between Geometry and Algebra in GIS 20.6 Development of Common Techniques in GIS 20.7 Integration of GIS and Remote Sensing 20.8 Integration of GIS and Multimedia 20.9 3D GIS 20.9.1 Virtual Reality in GIS 20.10 Integration of 3D GIS and Web GIS 20.11 4D GIS and Real-time GIS 20.12 Mobile GIS 20.12.1 Mobile mapping 20.13 Collaborative GIS (CGIS) 21. Change Detection and Geosimulation 21.1 Visual change detection 21.2 Thresholding 21.3 Image difference 21.4 Image regression 21.5 Image ratioing 21.6 Vegetation index differencing 21.7 Principal component differencing 21.8 Multi-temporal image stock classification 21.9 Post classification comparison 21.10 Change vector analysis 21.12 Cellular automata simulation 21.13 Multi-agent simulation 21.14 ANN learning in simulation Appendix A - Concept of Map, Coordinate System, and Projection Appendix B - Concept on Mathematical Topics Numéro de notice : 26518 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/POSITIONNEMENT Nature : Manuel de cours DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97342

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Reference system origin and scale realization within the future GNSS constellation “Kepler” / Susanne Glaser in Journal of geodesy, vol 94 n° 12 (December 2020)  

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Les stations virtuelles au service de la cartographie mobile / Mathieu Regul in XYZ, n° 165 (décembre 2020)

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Integrated processing of ground- and space-based GPS observations: improving GPS satellite orbits observed with sparse ground networks / Wen Huang in Journal of geodesy, vol 94 n° 10 (October 2020)  

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Evaluation of single-frequency receivers for studying crustal deformation at the longitudinal Valley fault, eastern Taiwan / Horng-Yue Chen in Survey review, vol 52 n° 374 (August 2020)  

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Automated estimation and tools to extract positions, velocities, breaks, and seasonal terms from daily GNSS measurements: illuminating nonlinear Salton Trough deformation / Michael B. Heflin in Earth and space science, vol 7 n° 7 (July 2020)  

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Benefits of combining GPS and GLONASS for measuring ocean tide loading displacement / Majid Abbaszadeh in Journal of geodesy, vol 94 n° 7 (July 2020)  

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GPS + Galileo + BeiDou precise point positioning with triple-frequency ambiguity resolution / Pan Li in GPS solutions, Vol 24 n° 3 (July 2020)  

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Orbit and clock analysis of BDS-3 satellites using inter-satellite link observations / Xin Xie in Journal of geodesy, vol 94 n° 7 (July 2020)  

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DORIS, 30 ans d'opérations continues au coeur de la performance des missions altimétriques pour l'océanographie et les applications géodésiques / Anonyme in XYZ, n° 163 (juin 2020)

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Geodetic VLBI for precise orbit determination of Earth satellites: a simulation study / Grzegorz Klopotek in Journal of geodesy, vol 94 n° 6 (June 2020)  

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