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Subseasonal GNSS positioning errors / Jim Ray in Geophysical research letters, vol 40 n° 22 (November 2013)
[article]
Titre : Subseasonal GNSS positioning errors Type de document : Article/Communication Auteurs : Jim Ray, Auteur ; Jake Griffiths, Auteur ; Xavier Collilieux , Auteur ; Paul Rebischung , Auteur Année de publication : 2013 Article en page(s) : pp 5854 - 5860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] bruit rose
[Termes IGN] coordonnées GNSS
[Termes IGN] crénelage
[Termes IGN] données GLONASS
[Termes IGN] élément d'orientation externe
[Termes IGN] variation saisonnière
[Vedettes matières IGN] Traitement de données GNSSRésumé : (auteur) Global Navigation Satellite System (GNSS) station coordinate errors over seasonal and longer time scales are known to be spatially and temporally correlated with flicker noise spectra. Overlaying this are strong annual and semiannual variations that cannot be explained by any single phenomenon. Next most prominent are harmonics of the GPS draconitic year with periods of (351.4/N) days. One explanation is that errors in the standard model for Earth orientation parameter (EOP) tidal variations near 12 and 24 h periods are absorbed into the resonant GPS orbit and daily EOP estimates, resulting mainly in draconitic and fortnightly alias signatures for 24 h product sampling. With the change in International GNSS Service (IGS) station coordinates from weekly to daily resolution in August 2012, it is now possible to study subseasonal performance. All IGS Analysis Centers (ACs) show fortnightly signals, but the resolution will not be sufficient to distinguish direct from aliased subdaily tidal error sources till two more years of data are available. Nevertheless, aliased errors from the subdaily EOP tide model are expected. All but one of the ACs that includes GLONASS data have signals at ~8 day periods, the ground repeat period for GLONASS orbits. This most likely arises from larger geographically correlated orbit errors for GLONASS. Two ACs possess unique short-period features that appear to be caused by peculiarities of their analysis strategies. Numéro de notice : A2013-795 Affiliation des auteurs : LASTIG LAREG+Ext (2012-mi2018) Thématique : POSITIONNEMENT Nature : Article DOI : 10.1002/2013GL058160 Date de publication en ligne : 08/11/2013 En ligne : http://dx.doi.org/10.1002/2013GL058160 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80235
in Geophysical research letters > vol 40 n° 22 (November 2013) . - pp 5854 - 5860[article]Documents numériques
en open access
Subseasonal GNSS Positionning ErrorsAdobe Acrobat PDF Modelos armonicos no lineales para series temporales geodéticas = Non-linear harmonic models for geodetic time series / P.A. Martinez-Ortiz (2011)
Titre : Modelos armonicos no lineales para series temporales geodéticas = Non-linear harmonic models for geodetic time series Type de document : Thèse/HDR Auteurs : P.A. Martinez-Ortiz, Auteur ; J.M. Fernadiz Leal, Directeur de thèse Editeur : Alicante : Escuella politécnica superior Année de publication : 2011 Importance : 402 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Espagnol (spa) Descripteur : [Vedettes matières IGN] Géodésie
[Termes IGN] analyse harmonique
[Termes IGN] analyse spectrale
[Termes IGN] bruit blanc
[Termes IGN] bruit rose
[Termes IGN] géocentre
[Termes IGN] marée terrestre
[Termes IGN] masse de la Terre
[Termes IGN] Matlab
[Termes IGN] modèle non linéaire
[Termes IGN] positionnement par GPS
[Termes IGN] série temporelle
[Termes IGN] système de référence géodésiqueIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) The dissertation addresses the development of new methods and software for the spectral analysis of scalar or vectorial time series, with emphasis in the applications of geodetic interest. The starting point can be placed in the method introduced by Harada and Fukushima for the non-linear analysis of time series, which allows the recursive detection of frequencies and their associated amplitudes and phases as well as the secular mixed Fourier terms when found in the signal. That method is extended in different ways, allowing the treatment of series affected by auto correlated noise with a power law, either evenly or unevenly spaced. This is made both at the level of frequency detection and non-linear fitting. Reduction of the computational overhead is also obtained. The theoretical work is accompanied by the developing of comprehensive and specialized software for such non-linear harmonic analysis of time series using the MATLAB programming language. Much of the tools we can find today for analyzing these periodic time series are valid only for certain types whereas the programs in the thesis can be applied to oddly spaced series in the presence of combinations of white and flicker noise. The new methods and routines are used for analyzing some interesting series as those ones describing the celestial pole offsets, the geocentre variations due to the redistribution of water mass on the Earth's surface, the excess of the length of day, continental water flux and the positions of GPS stations, among others. We estimate harmonic models that explain each one of these phenomena in the considered time domain and allow us to draw conclusions of their behavior. Note de contenu : Agradecimientos
Resumen
Abstract
I MODELOS ARMÓNICOS NO LINEALES PARA SE RÍES TEMPORALES GEODÉTICAS
1. Introducción
1.1. Objetivos y metodología
1.2. Contenidos
2. El problema de la detección de señales
2.1. Definición de serie temporal
2.1.1. Componentes de una serie temporal
2.1.2. Componente de ruido
2.2. Técnicas para el estudio de series temporales
2.2.1. Análisis clásico
2.2.2. Análisis espectral
2.2.3. Análisis wavelet
2.3. El problema de la detección de señales
2.3.1. Periodograma de Lomb
2.3.2. Dominio de frecuencia
3. Análisis armónico no lineal
3.1. Introducción
3.2. Descripción del método
3.2.1. Función objetivo y funciones base
3.2.2. Solución mínimo cuadrática
3.2.3. Optimización no lineal: Algoritmo BFGS
3.2.4. Extracción de frecuencias
3.3. Incertidumbre
3.4. Generalización
3.5. Tratamiento de los términos periódicos de corta frecuencia
4. Variaciones del polo celeste
4.1. Modelo de precesión-nutación IAU1980
4.1.1. Introducción
4.1.2. Descripción de los datos
4.1.3. Características del análisis y resultados
4.1.4. Conclusiones
4.2. Modelo de precesión-nutación IAU2000
4.2.1. Introducción
4.2.2. Descripción de los datos
4.2.3. Características del análisis y resultados
4.2.4. Conclusiones
4.3. Modelos dinámicos para la predicción a corto plazo de (óV óe)
5. Variaciones geocéntricas causadas por el flujo de agua continental 101
5.1. Introducción
5.2. Descripción de los datos
5.3. Características del análisis y resultados
5.4. Conclusiones
6. Modelos espacio-temporales para el flujo de agua continental
6.1. Introducción
6.2. Descripción de datos
6.3. Características del análisis
6.4. Resultados
6.5. Conclusiones
7. Estudio armónico del exceso en la duración del día
7.1. Introducción
7.2. Descripción de los datos
7.3. Características del análisis y resultados
7.4. Conclusiones
8. Ruido
8.1. Tipología básica
8.2. Matrices de covarianza
8.2.1. Matriz de covarianzas para un ruido blanco
8.2.2. Matriz de covarianzas para un ruido parpadeante
8.2.3. Matriz de covarianzas para un paseo aleatorio
8.3. Relación ruido-periodograma
8.4. Ruido en las observaciones GPS
9. Algoritmo FHAST
9.1. Introducción
9.2. Función objetivo
9.3. Modelo estocástico
9.3.1. Estimación de un índice espectral
9.3.2. Componente residual como combinación de varios ruidos. Es-timación de la frecuencia de transición,
9.4. Modelo funcional
9.5. Estimación de la componente de varianza
9.5.1. Condición de no negatividad
9.6. Extensión del periodograma
9.6.1. Aceleración del cálculo del periodograma
9.7. Incertidumbre
9.7.1. Parámetros lineales y no lineales del modelo funcional
9.7.2. Parámetros del modelo estocástico
9.8. Criterios de parada algorítmica
9.9. Entramado algorítmico
9.10. Simulación
10.Estudio de las series de posiciones de estaciones GPS
10.1. Introducción
10.2. Análisis de las series temporales residuales
10.3. Resultados y discusión
10.4. Conclusiones
11.Conclusiones y perspectivas
11.1. Conclusiones
11.2. Perspectivas
II EXTENDED SUMMARY: NON-LINEAR HARMO NIC MODELS FOR GEODETIC TIME SERIES
S.1. Introduction
S.2. The signal detection problem
S.2.1. Definition of time series
S.2.2. Spectral analysis
S.2.3. The signal detection problem
S.3. Non-linear harmonic analysis
S.4. Celestial Pole Offsets
S.4.1. IAU1980 Pole Offsets
S.4.2. IAU2000 Pole Offsets
S.4.3. Dynamic models for (óV,óe) prediction
S.5. Geocenter variations caused by continental water flux
S.5.1. Introduction
S.5.2. Data
S.5.3. Analysis and results
S.5.4. Conclusions
S.6. Spatio-temporal models for continental water flux
S.6.1. Introduction
S.6.2. Data
S.6.3. Methods
S.6.4. Results
S.6.5. Conclusions
S.7. Harmonio study of the length of day
S.7.1. Introduction
S.7.2. Data
S.7.3. Analysis and results
S.7.4. Conclusions
S.8. Noise
S.8.1. Typology
S.8.2. Covariance matrices
S.8.3. Relationship noise-periodogram
S.8.4. Noise in GPS observations
S.9. FHAST algorithm
S.9.1. Introduction
S.9.2. Stochastic model
S.9.3. Functional model
S.9.4. Component variance estimation
S.9.5. Modification of the periodogram
S.9.6. Stop criteria
S9.7. Algorithm
S.10. Study of the position time series of GPS stations
S.10.1.Introduction
S.10.2. Analysis and results
S.10.S.Conclusions
S.11 Conclusions and outlook
S.11.1.Conclusions
S.11.2. Outlook
III APÉNDICES
A. Ley de propagación del error
B, Acrónimos, abreviaturas y unidades
C. Modelos armónicos no lineales de algunas estaciones GPS
BibliografíaNuméro de notice : 10519 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse française Note de thèse : Bibliographie nature-HAL : Thèse DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=45141 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 10519-01 30.60 Livre Centre de documentation Géodésie Disponible Wavelet modelling of the gravity field by domain decomposition methods: an example over Japan / Isabelle Panet in Geophysical journal international, vol 184 n° 1 (January 2011)
[article]
Titre : Wavelet modelling of the gravity field by domain decomposition methods: an example over Japan Type de document : Article/Communication Auteurs : Isabelle Panet , Auteur ; Yuki Kuroishi, Auteur ; Matthias Holschneider, Auteur Année de publication : 2011 Article en page(s) : pp 203 - 219 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] analyse harmonique
[Termes IGN] bruit blanc
[Termes IGN] bruit rose
[Termes IGN] harmonique sphérique
[Termes IGN] itération
[Termes IGN] Japon
[Termes IGN] modèle de géopotentiel local
[Termes IGN] potentiel de pesanteur terrestre
[Termes IGN] transformation en ondelettesRésumé : (auteur) With the advent of satellite gravity, large gravity data sets of unprecedented quality at low and medium resolution become available. For local, high resolution field modelling, they need to be combined with the surface gravity data. Such models are then used for various applications, from the study of the Earth interior to the determination of oceanic currents. Here we show how to realize such a combination in a flexible way using spherical wavelets and applying a domain decomposition approach. This iterative method, based on the Schwarz algorithms, allows to split a large problem into smaller ones, and avoids the calculation of the entire normal system, which may be huge if high resolution is sought over wide areas. A subdomain is defined as the harmonic space spanned by a subset of the wavelet family. Based on the localization properties of the wavelets in space and frequency, we define hierarchical subdomains of wavelets at different scales. On each scale, blocks of subdomains are defined by using a tailored spatial splitting of the area. The data weighting and regularization are iteratively adjusted for the subdomains, which allows to handle heterogeneity in the data quality or the gravity variations. Different levels of approximations of the subdomains normals are also introduced, corresponding to building local averages of the data at different resolution levels. We first provide the theoretical background on domain decomposition methods. Then, we validate the method with synthetic data, considering two kinds of noise: white noise and coloured noise. We then apply the method to data over Japan, where we combine a satellite-based geopotential model, EIGEN-GL04S, and a local gravity model from a combination of land and marine gravity data and an altimetry-derived marine gravity model. A hybrid spherical harmonics/wavelet model of the geoid is obtained at about 15 km resolution and a corrector grid for the surface model is derived. Numéro de notice : A2011-604 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1365-246X.2010.04840.x Date de publication en ligne : 01/01/2011 En ligne : https://doi.org/10.1111/j.1365-246X.2010.04840.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91678
in Geophysical journal international > vol 184 n° 1 (January 2011) . - pp 203 - 219[article]Documents numériques
en open access
Wavelet modelling of the gravity field .. - pdf éditeurAdobe Acrobat PDF Reflecting on GPS: sensing land and ice from low Earth orbit / S.T. Gleason in GPS world, vol 18 n° 10 (October 2008)
[article]
Titre : Reflecting on GPS: sensing land and ice from low Earth orbit Type de document : Article/Communication Auteurs : S.T. Gleason, Auteur Année de publication : 2007 Article en page(s) : pp 44 - 49 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] bruit rose
[Termes IGN] glace
[Termes IGN] réflectance
[Termes IGN] signal GPS
[Termes IGN] surveillance de la végétation
[Termes IGN] traitement du signalRésumé : (Editeur) An out-of-the-ordinary application of GPS uses signals reflected of the Earth's surface to sense land and ice, as well as ocean surface, from low Earth orbit. Analysis of these signals holds great promise for measuring ocean roughness, ice conditions, vegetation cover, and even soil moisture. Copyright Questex Media Group Inc Numéro de notice : A2007-478 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28841
in GPS world > vol 18 n° 10 (October 2008) . - pp 44 - 49[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 067-07101 SL Revue Centre de documentation Revues en salle Disponible
Titre : Least-square variance component estimation : Theory and GPS applications Type de document : Thèse/HDR Auteurs : Ali Reza Amiri-Simkooei, Auteur Editeur : Delft : Netherlands Geodetic Commission NGC Année de publication : 2007 Collection : Netherlands Geodetic Commission Publications on Geodesy, ISSN 0165-1706 num. 64 Importance : 208 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-90-6132-301-3 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] analyse de variance
[Termes IGN] analyse multivariée
[Termes IGN] bruit blanc
[Termes IGN] bruit rose
[Termes IGN] coordonnées GPS
[Termes IGN] estimation statistique
[Termes IGN] matrice de covariance
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle stochastique
[Termes IGN] série temporelle
[Termes IGN] signal GPS
[Termes IGN] varianceIndex. décimale : 30.61 Systèmes de Positionnement par Satellites du GNSS Résumé : (Auteur) Data processing in geodetic applications often relies on the least-squares method, for which one needs a proper stochastic model of the observables. Such a realistic covariance matrix allows one first to obtain the best (minimum variance) linear unbiased estimator of the unknown parameters; second, to determine a realistic precision description of the unknowns; and, third, along with the distribution of the data, to correctly perform hypothesis testing and assess quality control measures such as reliability. In many practical applications the covariance matrix is only partly known. The covariance matrix is then usually written as an unknown linear combination of known cofactor matrices. The estimation of the unknown (co)variance components is generally referred to as variance component estimation (VCE). In this thesis we study the method of least-squares variance component estimation (LSVCE) and elaborate on theoretical and practical aspects of the method. We show that LS-VCE is a simple, flexible, and attractive VCE-method. The LS-VCE method is simple because it is based on the well-known principle of least-squares. With this method the estimation of the (co)variance components is based on a linear model of observation equations. The method is flexible since it works with a user-defined weight matrix. Different weight matrix classes can be defined which all automatically lead to unbiased estimators of (co)variance components. LS-VCE is attractive since it allows one to apply the existing body of knowledge of least-squares theory to the problem of (co)variance component estimation. With this method, one can 1) obtain measures of discrepancies in the stochastic model, 2) determine the covariance matrix of the (co)variance components, 3) obtain the minimum variance estimator of (co)variance components by choosing the weight matrix as the inverse of the covariance matrix, 4) take the a-priori information on the (co)variance component into account, 5) solve for a nonlinear (co)variance component model, 6) apply the idea of robust estimation to (co)variance components, 7) evaluate the estimability of the (co)variance components, and 8) avoid the problem of obtaining negative variance components. LS-VCE is capable of unifying many of the existing VCE-methods such as MINQUE, BIQUE, and REML, which can be recovered by making appropriate choices for the weight matrix. An important feature of the LS-VCE method is the capability of applying hypothesis testing to the stochastic model, for which we rely on the w-test, v-test, and overall model test. We aim to find an appropriate structure for the stochastic model which includes the relevant noise components into the covariance matrix. The w-test statistic is introduced to see whether or not a certain noise component is likely to be present in the observations, which consequently can be included in the stochastic model. Based on the normal distribution of the original observables we determine the mean and the variance of the w-test statistic, which are zero and one, respectively. The distribution is a linear combination of mutually independent central chi-square distributions each with one degree of freedom. This distribution can be approximated by the standard normal distribution for some special cases. An equivalent expression for the w-test is given by introducing the v-test statistic. The goal is to decrease the number of (co)variance components of the stochastic model by testing the significance of the components. The overall model test is introduced to generally test the appropriateness of a proposed stochastic model. We also apply LS-VCE to real data of two GPS applications. LS-VCE is applied to the GPS geometry-free model. We present the functional and stochastic model of the GPS observables. The variance components of different observation types, satellite elevation dependence of GPS observables’ precision, and correlation between different observation types are estimated by LS-VCE. We show that the precision of the GPS observables clearly depends on the elevation angle of satellites. Also, significant correlation between observation types is found. For the second application we assess the noise characteristics of time series of daily coordinates for permanent GPS stations. We apply LS-VCE to estimate white noise and power-law noise (flicker noise and random walk noise) amplitudes in these time series. The results confirm that the time series are highly time correlated. We also use the w-test statistic to find an appropriate stochastic model of GPS time series. A combination of white noise, autoregressive noise, and flicker noise in general best characterizes the noise in all three position components. Unmodelled periodic effects in the data are then captured by a set of harmonic functions, for which we rely on least-squares harmonic estimation (LS-HE) developed in the same framework as LS-VCE. The results confirm the presence of annual and semiannual signals, as well as other significant periodic patterns in the series. To avoid the biased estimation of the variance components, such sinusoidal signals should be included in the functional part of the model before applying LS-VCE. Note de contenu : 1. Introduction
2. Least-Squares Estimation and Validation
3. Variance Component Estimation: A Review
4. Least-Squares Variance Component Estimation
5. Detection and Validation in Stochastic Model
6. Multivariate Variance-Covariance Analysis
7. GPS Geometry-Free Model
8. GPS Coordinate Time Series
9. Conclusions and Recommendations
A. Mathematical Background
B. Derivation of Equations
C. Moments of Normally Distributed Data
D. Mixed model with hard constraints
BibliographyNuméro de notice : 15303 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse étrangère DOI : sans En ligne : https://www.ncgeo.nl/downloads/71Memarzadeh.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=62689 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 15303-01 30.61 Livre Centre de documentation Géodésie Disponible Error analysis of weekly station coordinates in the DORIS network / Simon D.P. Williams in Journal of geodesy, vol 80 n° 8-11 (November 2006)PermalinkEstimating the noise in space-geodetic positioning: the case of DORIS / Karine Le Bail in Journal of geodesy, vol 80 n° 8-11 (November 2006)PermalinkPlate kinematic of Nubia-Somalia using combined DORIS and GPS solution / Jean-Mathieu Nocquet in Journal of geodesy, vol 80 n° 8-11 (November 2006)PermalinkPermalinkLong term consistency of multi-technique terrestrial reference frames, a spectral approach / Karine Le Bail (2006)PermalinkLes détecteurs de rayonnement infra-rouge / G. Chol (1966)Permalink