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Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series / Kevin Gobron in Journal of geodesy, vol 96 n° 7 (July 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 7 (July 2022) . - n° 46 Titre : Impact of offsets on assessing the low-frequency stochastic properties of geodetic time series Type de document : Article/Communication Auteurs : Kevin Gobron, Auteur ; Paul Rebischung , Auteur ; Olivier de Viron, Auteur ; Alain Demoulin, Auteur ; Michel Van Camp, Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : n° 46 Note générale : bibliographie This study has been financially supported by the Direction Générale de l’Armement (DGA), the Nouvelle-Aquitaine region, and the Centre National des Etudes Spatiales (CNES) as an application of the geodesy missions. This research was also supported by the Brain LASUGEO project entitled “monitoring LAnd SUbsidence caused by Groundwater exploitation through gEOdetic measurements” funded by the Belgian Sciences Policy. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] analyse de variance [Termes IGN] bruit blanc [Termes IGN] fréquence [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] modèle de Gauss-Markov [Termes IGN] modèle stochastique [Termes IGN] série temporelle [Termes IGN] vitesse Résumé : (auteur) Understanding and modelling the properties of the stochastic variations in geodetic time series is crucial to obtain realistic uncertainties for deterministic parameters, e.g., long-term velocities, and helpful in characterizing non-modelled processes. With the increasing span of geodetic time series, it is expected that additional observations would help better understand the low-frequency properties of these stochastic variations. In the meantime, recent studies evidenced that the choice of the functional model for the time series biases the assessment of these low-frequency stochastic properties. In particular, frequent offsets in position time series can hinder the evaluation of the noise level at low frequencies and prevent the detection of possible random-walk-type variability. This study investigates the ability of the Maximum Likelihood Estimation (MLE) method to correctly retrieve low-frequency stochastic properties of geodetic time series in the presence of frequent offsets. We show that part of the influence of offsets reported by previous studies results from the MLE method estimation biases. These biases occur even when all offset epochs are correctly identified and accounted for in the trajectory model. They can cause a dramatic underestimation of deterministic parameter uncertainties. We show that one can avoid biases using the Restricted Maximum Likelihood Estimation (RMLE) method. Yet, even when using the RMLE method or equivalent, adding offsets to the trajectory model inevitably blurs the estimated low-frequency properties of geodetic time series by increasing low-frequency stochastic parameter uncertainties more than other stochastic parameters. Numéro de notice : A2022-519 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-022-01634-9 Date de publication en ligne : 29/06/2022 En ligne : https://doi.org/10.1007/s00190-022-01634-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101072 [article]

GNSS/INS Kalman filter integrity monitoring with uncertain time correlated error processes / Omar Garcia Crespillo (2022)  

Public Titre : GNSS/INS Kalman filter integrity monitoring with uncertain time correlated error processes Type de document : Thèse/HDR Auteurs : Omar Garcia Crespillo, Auteur ; Jan Skaloud, Directeur de thèse ; Michael Meurer, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2022 Importance : 180 p. Format : 21 x 30 cm Note générale : bibliographie Thèse présentée pour l'obtention du grade de Docteur ès Sciences Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes IGN] corrélation temporelle [Termes IGN] couplage GNSS-INS [Termes IGN] filtre de Kalman [Termes IGN] fréquence multiple [Termes IGN] modèle d'erreur [Termes IGN] modèle de Gauss-Markov [Termes IGN] navigation inertielle [Termes IGN] norme [Termes IGN] positionnement par GNSS [Termes IGN] Receiver Autonomous Integrity Monitoring [Termes IGN] système d'extension Résumé : (auteur) Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. Over the last decade, significant effort has been put to guarantee a bounded position estimation by using Global Navigation Satellite Systems (GNSS) by means of satellite-based or ground-based augmentation systems (SBAS, GBAS) and Advanced Receiver Autonomous Integrity Monitoring (ARAIM) for aviation. This has been achieved by carefully designing models that overbound the different residual error components in range measurements (e.g., satellite clock and orbit, tropospheric and multipath among others). On the other hand, and as part of Aircraft based Augmentation Systems (ABAS), the use of Inertial Reference Systems (IRS) has been traditionally included as additional source of redundant navigation information. More recently, the use of Inertial Navigation Systems (INS) with a wider spectrum of possible inertial sensor qualities in tighter integration with single-frequency GNSS has seen its way in a new Minimum Operational Performance Standard (MOPS). New GNSS/INS systems and standards could still benefit from the methodologies and aspects developed for future dual-frequency/multiconstellation GNSS standards. However, safety-related GNSS systems like ARAIM are snapshot-based, that is, the position estimation is performed independently at every epoch, whereas GNSS/INS systems are typically based on Kalman filtering (KF). Therefore, the existing error overbounding models and methodologies are not enough to produce a robust KF position estimation since the impact of time-correlation in measurements must also be accounted for. Moreover, it has been observed that the time-correlation of different GNSS errors presents also some level of uncertain behavior, which makes very challenging for linear dynamic systems to produce a guaranteed solution. As proposed by GNSS Minimum Operational Performance Standards (MOPS), there are sources of time-correlated errors that can be well modelled using a first order Gauss-Markov process (GMP). Using this GMP parametric model, it is possible to capture the uncertain timecorrelated nature of error processes by allowing the variance and time correlation constant of the GMP model to be in a bounded range. Under this situation, the first part of this thesis studies the propagation of the uncertain models through the Kalman filter estimation and provides new theoretical tools in time and frequency domain to bound the KF error estimation covariance. As a result, tight stationary bounding models on the GMP uncertain processes are derived in both continuous and discrete time domain. This is extended to non-stationary models that provide tighter error bounding during an initial transient phase when measurements are first introduced (which will be relevant in scenarios with changing number of visible satellites). The new models can very easily be used during the KF implementation which might be very attractive by regulators and designers. In the second part of the thesis, the new overbounding GMP models are applied for a dual-frequency GPS-Galileo tightly-coupled GNSS/INS integration. The design of the filter and of error models is performed following compatibility with current aviation standards and ARAIM Working Group C results. The impact of the use of the new models is analysed in terms of conservativeness, integrity and continuity based on realistic operational simulations linked to airport runways. The benefit of an overbounded GNSS/INS solution is also compared with the current baseline ARAIM algorithm solution. This thesis supports the evolution of safe GNSS-based positioning systems from only snapshot based to filtered solutions. Ensuring integrity for Kalman filter in general and for GNSS/INS systems in particular is a game changer to achieve higher performance levels for future dualfrequency multi-constellation aviation services and is of vital importance for new ground and air applications like autonomous vehicles or urban air mobility. Note de contenu : Introduction 1- Preliminaries 2- Bounding Kalman Filter with uncertain error processes 3- Application to GNSS/INS integraty monitoring 4- Closing 5- Appendix Numéro de notice : 28688 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Sciences : Lausanne : 2022 DOI : sans En ligne : https://infoscience.epfl.ch/record/292087?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100103

Least squares adjustment with a rank-deficient weight matrix and Its applicability to image/Lidar data processing / Radhika Ravi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 10 (October 2021) . - pp 717 - 733 Titre : Least squares adjustment with a rank-deficient weight matrix and Its applicability to image/Lidar data processing Type de document : Article/Communication Auteurs : Radhika Ravi, Auteur ; Ayman Habib, Auteur Année de publication : 2021 Article en page(s) : pp 717 - 733 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] alignement des données [Termes IGN] chevauchement [Termes IGN] compensation par moindres carrés [Termes IGN] données lidar [Termes IGN] lidar mobile [Termes IGN] matrice [Termes IGN] matrice de covariance [Termes IGN] modèle de Gauss-Helmert [Termes IGN] modèle de Gauss-Markov [Termes IGN] modèle mathématique [Termes IGN] modélisation 3D [Termes IGN] pondération [Termes IGN] semis de points Résumé : (Auteur) This article proposes a solution to special least squares adjustment (LSA) models with a rank-deficient weight matrix, which are commonly encountered in geomatics. The two sources of rank deficiency in weight matrices are discussed: naturally occurring due to the inherent characteristics of LSA mathematical models and artificially induced to eliminate nuisance parameters from LSA estimation. The physical interpretation of the sources of rank deficiency is demonstrated using a case study to solve the problem of 3D line fitting, which is often encountered in geomatics but has not been addressed fully to date. Finally, some geomatics-related applications—mobile lidar system calibration, point cloud registration, and single-photo resection—are discussed along with respective experimental results, to emphasize the need to assess LSA models and their weight matrices to draw inferences regarding the effective contribution of observations. The discussion and results demonstrate the vast applications of this research in geomatics as well as other engineering domains. Numéro de notice : A2021-675 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00081R3 Date de publication en ligne : 10/01/2021 En ligne : https://doi.org/10.14358/PERS.20-00081R3 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98861 [article]

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Gauss-Markov model with random parameters to adjust results of surveys of geodetic control networks / Marek Banas in Reports on geodesy and geoinformatics, vol 111 n° 1 (June 2021)  

Public [article]  inReports on geodesy and geoinformatics > vol 111 n° 1 (June 2021) Titre : Gauss-Markov model with random parameters to adjust results of surveys of geodetic control networks Type de document : Article/Communication Auteurs : Marek Banas, Auteur ; Jozef Czaja, Auteur ; Janusz Dabrowski, Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux [Termes IGN] matrice de covariance [Termes IGN] modèle de Gauss-Markov [Termes IGN] point de nivellement [Termes IGN] réseau de contrôle Résumé : (auteur) Alignment of an engineering object project in the field is always conducted at the points of the geodetic control network, the coordinates of which are determined on the basis of the results of its elements survey and with connection to the national spatial reference system. The points of the national spatial reference system determined on the basis of previous surveys have specified coordinates with adequate accuracy, which is included in their covariance matrix. The coordinates of the geodetic control network points are determined more accurately than the points of the national spatial reference system and this means that the results of surveys of the geodetic control network have to be adequately incorporated into the coordinates of the reference points. In order to perform this incorporation, it may be assumed that the coordinates of the reference points are random, that is, they have a covariance matrix, which should be used in the process of adjusting the results of the geodetic control network observation. This research paper presents the principles for the estimation of the Gauss-Markov model parameters applied in case of those geodetic control networks in which the coordinates of the reference points have random character. On the basis of the observation equations δ + AX = L for the geodetic control network and using the weighting matrix P and the matrix of conditional covariances (P−1 + ACXAT) for the observation vector L, the parameter vector X is estimated in the form of the derived formula X^=(CX−1+ATPA)−1ATP⋅L . The verification of these estimation principles has been illustrated by the example of a fragment of a levelling geodetic control network consisting of three geodetic control points and two reference points of the national spatial reference system. The novel feature of the proposed solution is the application of covariance matrices of the reference point coordinates to adjust the results of the survey of geodetic control networks and to determine limit standard deviations for the estimated coordinates of geodetic control network points. Numéro de notice : A2021-780 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.2478/rgg-2021-0001 En ligne : https://doi.org/10.2478/rgg-2021-0001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98797 [article]

Assessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery / Allison Lassiter in Plos one, vol 15 n° 5 (May 2020)  

Public [article]  inPlos one > vol 15 n° 5 (May 2020) Titre : Assessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery Type de document : Article/Communication Auteurs : Allison Lassiter, Auteur ; Mayank Darbari, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] arbre urbain [Termes IGN] classification barycentrique [Termes IGN] classification par réseau neuronal [Termes IGN] forêt urbaine [Termes IGN] image aérienne [Termes IGN] image multibande [Termes IGN] modèle de Gauss-Markov [Termes IGN] segmentation d'image Résumé : (auteur) To analyze types and patterns of greening trends across a city, this study seeks to identify a method of creating very high-resolution urban vegetation maps that scales over space and time. Vegetation poses unique challenges for image segmentation because it is patchy, has ragged boundaries, and high in-class heterogeneity. Existing and emerging public datasets with the spatial resolution necessary to identify granular urban vegetation lack a depth of affordable and accessible labeled training data, making unsupervised segmentation desirable. This study evaluates three unsupervised methods of segmenting urban vegetation: clustering with k-means using k-means++ seeding; clustering with a Gaussian Mixture Model (GMM); and an unsupervised, backpropagating convolutional neural network (CNN) with simple iterative linear clustering superpixels. When benchmarked against internal validity metrics and hand-coded data, k-means is more accurate than GMM and CNN in segmenting urban vegetation. K-means is not able to differentiate between water and shadows, however, and when this segment is important GMM is best for probabilistically identifying secondary land cover class membership. Though we find the unsupervised CNN shows high degrees of accuracy on built urban landscape features, its accuracy when segmenting vegetation does not justify its complexity. Despite limitations, for segmenting urban vegetation, k-means has the highest performance, is the simplest, and is more efficient than alternatives. Numéro de notice : A2020-834 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article DOI : 10.1371/journal.pone.0230856 Date de publication en ligne : 07/05/2020 En ligne : https://doi.org/10.1371/journal.pone.0230856 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97668 [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)  

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Photogrammetric computer vision / Wolfgang Förstner (2016)

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Proposed design of local 2D geodetic network for the construction of the tunnel part of the Belgrade metro / Marija Savanović, in Geodetski vestnik, vol 59 n° 3 (September - November 2015)  

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Transformation model selection by multiple hypotheses testing / Rüdiger Lehmann in Journal of geodesy, vol 88 n° 12 (December 2014)  

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Correlated errors in GPS position time series: Implications for velocity estimates / Alvaro Santamaria Gomez in Journal of geophysical research : Solid Earth, Vol 116 n° B1 (January 2011)  

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Analyse de données lidar à retour d'onde complète pour la classification en milieu urbain = Analysis of Full-Waveform lidar data for urban area mapping / Clément Mallet (2010)  

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Advanced full-waveform lidar data echo detection: assessing quality of derived terrain and tree height models in an alpine coniferous forest / Adrien Chauve in International Journal of Remote Sensing IJRS, vol 30 n° 19 (October 2009)    

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Problèmes inverses en imagerie et en vision, 2. Volume 2 / A. Mohammad-Djafari (2009)

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Least squares 3D surface and curve matching / Armin W. Gruen in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 3 (May 2005)  

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Solving algebraic computational problems in geodesy and geoinformatics / Joseph L. Awange (2005)

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