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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]

Robust external calibration of terrestrial laser scanner and digital camera for structural monitoring / Mohammad Omidalizarandi in Journal of applied geodesy, vol 13 n° 2 (April 2019)  

Public [article]  inJournal of applied geodesy > vol 13 n° 2 (April 2019) . - pp 105 - 130 Titre : Robust external calibration of terrestrial laser scanner and digital camera for structural monitoring Type de document : Article/Communication Auteurs : Mohammad Omidalizarandi, Auteur ; Boris Kargoll, Auteur ; Jens-André Paffenholz, Auteur ; Ingo Neumann, Auteur Année de publication : 2019 Article en page(s) : pp 105 - 130 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme espérance-maximisation [Termes IGN] déformation de la croute terrestre [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] élément d'orientation externe [Termes IGN] méthode des moindres carrés [Termes IGN] modèle de Gauss-Helmert [Termes IGN] modèle de Gauss-Markov [Termes IGN] orthoimage [Termes IGN] semis de points Résumé : (Auteur) In the last two decades, the integration of a terrestrial laser scanner (TLS) and digital photogrammetry, besides other sensors integration, has received considerable attention for deformation monitoring of natural or man-made structures. Typically, a TLS is used for an area-based deformation analysis. A high-resolution digital camera may be attached on top of the TLS to increase the accuracy and completeness of deformation analysis by optimally combining points or line features extracted both from three-dimensional (3D) point clouds and captured images at different epochs of time. For this purpose, the external calibration parameters between the TLS and digital camera needs to be determined precisely. The camera calibration and internal TLS calibration are commonly carried out in advance in the laboratory environments. The focus of this research is to highly accurately and robustly estimate the external calibration parameters between the fused sensors using signalised target points. The observables are the image measurements, the 3D point clouds, and the horizontal angle reading of a TLS. In addition, laser tracker observations are used for the purpose of validation. The functional models are determined based on the space resection in photogrammetry using the collinearity condition equations, the 3D Helmert transformation and the constraint equation, which are solved in a rigorous bundle adjustment procedure. Three different adjustment procedures are developed and implemented: (1) an expectation maximization (EM) algorithm to solve a Gauss-Helmert model (GHM) with grouped t-distributed random deviations, (2) a novel EM algorithm to solve a corresponding quasi-Gauss-Markov model (qGMM) with t-distributed pseudo-misclosures, and (3) a classical least-squares procedure to solve the GHM with variance components and outlier removal. The comparison of the results demonstrates the precise, reliable, accurate and robust estimation of the parameters in particular by the second and third procedures in comparison to the first one. In addition, the results show that the second procedure is computationally more efficient than the other two. Numéro de notice : A2019-145 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2018-0038 Date de publication en ligne : 02/02/2019 En ligne : https://doi.org/10.1515/jag-2018-0038 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92472 [article]

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

Public contenu dans The 1st International workshop on the quality of geodetic observation and monitoring systems (QuGOMS'11) / Hansjörg Kutterer (2015)   Titre : Magic square of real spectral and time series analysis with an application to moving average processes Type de document : Article/Communication Auteurs : I. Krasbutter, Auteur ; Boris Kargoll, Auteur ; W.D. Schuh, Auteur 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 : pp 9 - 14 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] analyse spectrale [Termes IGN] moyenne mobile [Termes IGN] processus stochastique [Termes IGN] série temporelle Résumé : (auteur) This paper is concerned with the spectral analysis of stochastic processes that are realvalued, one-dimensional, discrete-time, covariance-stationary, and which have a representation as a moving average (MA) process. In particular, we will review the meaning and interrelations of four fundamental quantities in the time and frequency domain, (1) the stochastic process itself (which includes filtered stochastic processes), (2) its autocovariance function, (3) the spectral representation of the stochastic process, and (4) the corresponding spectral distribution function, or if it exists, the spectral density function. These quantities will be viewed as forming the corners of a square (the “magic square of spectral and time series analysis”) with various connecting lines, which represent certain mathematical operations between them. To demonstrate the evaluation of these operations, we will discuss the example of a q-th order MA process. Numéro de notice : C2011-031 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Communication DOI : 10.1007/978-3-319-10828-5_2 En ligne : https://doi.org/10.1007/978-3-319-10828-5_2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84802