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Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors / Boris Kargoll in Journal of geodesy, vol 94 n° 5 (May 2020)  

Public [article]  inJournal of geodesy > vol 94 n° 5 (May 2020) Titre : Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors Type de document : Article/Communication Auteurs : Boris Kargoll, Auteur ; Gaël Kermarrec, Auteur ; Hamza Alkhatib, Auteur ; Johannes Korte, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] algorithme espérance-maximisation [Termes IGN] analyse vectorielle [Termes IGN] auto-régression [Termes IGN] bruit blanc [Termes IGN] corrélation croisée normalisée [Termes IGN] erreur aléatoire [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle stochastique [Termes IGN] régression linéaire [Termes IGN] série temporelle [Termes IGN] station GPS [Termes IGN] valeur aberrante Résumé : (auteur) The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN). Numéro de notice : A2020-291 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01376-6 Date de publication en ligne : 10/05/2020 En ligne : https://doi.org/10.1007/s00190-020-01376-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95120 [article]

The stochastic model for Global Navigation Satellite Systems and terrestrial laser scanning observations: A proposal to account for correlations in least squares adjustment / Gaël Kermarrec in Journal of applied geodesy, vol 13 n° 2 (April 2019)  

Public [article]  inJournal of applied geodesy > vol 13 n° 2 (April 2019) . - pp 93 - 104 Titre : The stochastic model for Global Navigation Satellite Systems and terrestrial laser scanning observations: A proposal to account for correlations in least squares adjustment Type de document : Article/Communication Auteurs : Gaël Kermarrec, Auteur ; Ingo Neumann, Auteur ; Hamza Alkhatib, Auteur ; Steffen Schön, Auteur Année de publication : 2019 Article en page(s) : pp 93 - 104 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] analyse de variance [Termes IGN] compensation par faisceaux [Termes IGN] compensation par moindres carrés [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] matrice [Termes IGN] modèle stochastique Résumé : (Auteur) The best unbiased estimates of unknown parameters in linear models have the smallest expected mean-squared errors as long as the residuals are weighted with their true variance–covariance matrix. As this condition is rarely met in real applications, the least-squares (LS) estimator is less trustworthy and the parameter precision is often overoptimistic, particularly when correlations are neglected. A careful description of the physical and mathematical relationships between the observations is, thus, necessary to reach a realistic solution and unbiased test statistics. Global Navigation Satellite Systems and terrestrial laser scanners (TLS) measurements show similarities and can be both processed in LS adjustments, either for positioning or deformation analysis. Thus, a parallel between stochastic models for Global Navigation Satellite Systems observations proposed previously in the case of correlations and functions for TLS range measurements based on intensity values can be drawn. This comparison paves the way for a simplified way to account for correlations for a use in LS adjustment. Numéro de notice : A2019-144 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2018-0019 Date de publication en ligne : 24/01/2019 En ligne : https://doi.org/10.1515/jag-2018-0019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92471 [article]

Robust spatial approximation of laser scanner point clouds by means of Free-form Curve approaches in deformation analysis / Johannes Bureick in Journal of applied geodesy, vol 10 n° 1 (March 2016)  

Public [article]  inJournal of applied geodesy > vol 10 n° 1 (March 2016) . - pp 27 - 35 Titre : Robust spatial approximation of laser scanner point clouds by means of Free-form Curve approaches in deformation analysis Type de document : Article/Communication Auteurs : Johannes Bureick, Auteur ; Hamza Alkhatib, Auteur ; Ingo Neumann, Auteur Année de publication : 2016 Article en page(s) : pp 27 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] approximation [Termes IGN] B-Spline [Termes IGN] déformation géométrique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] estimation statistique [Termes IGN] méthode de Monte-Carlo [Termes IGN] méthode robuste [Termes IGN] semis de points Résumé : (auteur) In many geodetic engineering applications it is necessary to solve the problem of describing a measured data point cloud, measured, e. g. by laser scanner, by means of free-form curves or surfaces, e. g., with B-Splines as basis functions. The state of the art approaches to determine B-Splines yields results which are seriously manipulated by the occurrence of data gaps and outliers. Optimal and robust B-Spline fitting depend, however, on optimal selection of the knot vector. Hence we combine in our approach Monte-Carlo methods and the location and curvature of the measured data in order to determine the knot vector of the B-Spline in such a way that no oscillating effects at the edges of data gaps occur. We introduce an optimized approach based on computed weights by means of resampling techniques. In order to minimize the effect of outliers, we apply robust M-estimators for the estimation of control points. The above mentioned approach will be applied to a multi-sensor system based on kinematic terrestrial laserscanning in the field of rail track inspection. Numéro de notice : A2016-551 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/jag-2015-0020 En ligne : http://dx.doi.org/10.1515/jag-2015-0020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81696 [article]

The 1st International workshop on the quality of geodetic observation and monitoring systems (QuGOMS'11) / Hansjörg Kutterer (2015)  

Public Titre : The 1st International workshop on the quality of geodetic observation and monitoring systems (QuGOMS'11) : Proceedings of the 2011 IAG International Worshop, Munich, Germany, April 13-15, 2011 Type de document : Actes de congrès Auteurs : Hansjörg Kutterer, Éditeur scientifique ; Florian Seitz, Éditeur scientifique ; Hamza Alkhatib, Éditeur scientifique ; et al., Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2015 Collection : International Association of Geodesy Symposia, ISSN 0939-9585 num. 140 Conférence : QuGOMS 2011, 1st IAG International workshop on the quality of geodetic observation and monitoring systems 13/04/2011 15/04/2011 Munich Allemagne Proceedings Springer Importance : 183 p. Format : 21 x 29 cm ISBN/ISSN/EAN : 978-3-319-10827-8 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] ajustement de paramètres [Termes IGN] données géodésiques [Termes IGN] méthode des moindres carrés [Termes IGN] modèle d'incertitude [Termes IGN] positionnement par géodésie spatiale [Termes IGN] surveillance Index. décimale : CG2011 Actes de congrès en 2011 Note de contenu : PART 1 -- Uncertainty Modeling of Geodetic Data - Modeling Data Quality Using Artificial Neural Networks / Ralf Laufer and Volker Schwieger - Magic Square of Real Spectral and Time Series Analysis with an Application to Moving Average Processes / I Krasbutter, B Kargoll, and W-D Schuh - Describing the Quality of Inequality Constrained Estimates / L Roese-Koerner, B Devaraju,W-D Schuh, and N Sneeuw - GNSS Integer Ambiguity Validation Procedures: Sensitivity Analysis /J Wang and T Li - Optimal Design of Deformation Monitoring Networks Using the Global Optimization Methods / M Yetkin and C Inal PART 2 -- Theoretical Studies on Combination Strategies and Parameter Estimation - Towards the Combination of Data Sets from Various Observation Techniques / M Schmidt, F Göttl, and R Heinkelmann - On the Weighted Total Least Squares Solutions / X Fang and H Kutterer - Integration of Observations and Models in a Consistent Least Squares Adjustment Model / A Heiker and H Kutterer - Comparison of Different Combination Strategies Applied for the Computation of Terrestrial Reference Frames and Geodetic Parameter Series / Manuela Seitz - W-Ratio Test as an Integer Aperture Estimator: Pull-in Regions and Ambiguity Validation Performance / T Li and J Wang - Performing 3D Similarity Transformation Using theWeighted Total Least-Squares Method / J Lu, Y Chen, X Fang, and B Zheng - Comparison of Spatial Analyzer and Different Adjustment Programs / C Herrmann, M Lösler, and H Bähr PART 3 -- Recursive State-Space Filtering - State-Space Filtering with Respect to Data Imprecision and Fuzziness / I Neumann and H Kutterer - Unscented Kalman Filter Algorithm with Colored Noise and Its Application in Spacecraft Attitude Estimation / Lifen Sui, Zhongkai Mou, Yu Gan, and Xianyuan Huang - Principles and Comparisons of Various Adaptively Robust Filters with Applications in Geodetic Positioning / Yuanxi Yang, Tianhe Xu, and Junyi Xu - Alternative Nonlinear Filtering Techniques in Geodesy for Dual State and Adaptive Parameter Estimation / H Alkhatib PART 4 -- Sensor Networks and Multi Sensor Systems in Engineering Geodesy - Parametric Modeling of Static and Dynamic Processes in Engineering Geodesy / A Eichhorn - Land Subsidence in Mahyar Plain, Central Iran, Investigated Using Envisat SAR Data / M Davoodijam, M Motagh, and M Momeni - Recent Impacts of Sensor Network Technology on Engineering Geodesy / O Heunecke - Design of Artificial Neural Networks for Change-Point Detection / H Neuner - Spatial and Temporal Kinematics of the Inylchek Glacier in Kyrgyzstan Derived from Landsat and ASTER Imagery / M Nobakht, M Motagh, H.U Wetzel, and M.A Sharifi - Response Automation in Geodetic Sensor Networks by Means of Bayesian Networks / S Horst, H Alkhatib, and H Kutterer - Efficiency Optimization of Surveying Processes / Ivon Gösseln and H Kutterer - Modeling and Propagation of Quality Parameters in Engineering Geodesy Processes in Civil Engineering / Jürgen Schweitzer and Volker Schwieger PART 5 -- Multi-Mission Approaches with View to Physical Processes in the Earth System - Completion of Band-Limited Data Sets on the Sphere / W-D Schuh, S Müller, and J.M Brockmann Numéro de notice : 15830 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Actes DOI : 10.1007/978-3-319-10828-5 En ligne : https://doi.org/10.1007/978-3-319-10828-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75494

Contient

• Magic square of real spectral and time series analysis with an application to moving average processes / I. Krasbutter (2015)  

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
15830-01	CG2011	Livre	Centre de documentation	Congrès	Disponible