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Bayesian statistics and Monte Carlo methods / Karl Rudolf Koch in Journal of geodetic science, vol 8 n° 1 (January 2018)
[article]
Titre : Bayesian statistics and Monte Carlo methods Type de document : Article/Communication Auteurs : Karl Rudolf Koch, Auteur Année de publication : 2018 Article en page(s) : pp 18 - 29 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] matrice de covariance
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] propagation d'erreur
[Termes IGN] théorème de Bayes
[Termes IGN] variable aléatoire
[Termes IGN] vecteur aléatoire multidimensionnelRésumé : (Auteur) The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is defined as a measure of the plausibility of statements or propositions. Three rules are sufficient to obtain the laws of probability. If the statements refer to the numerical values of variables, the so-called random variables, univariate and multivariate distributions follow. They lead to the point estimation by which unknown quantities, i.e. unknown parameters, are computed from measurements. The unknown parameters are random variables, they are fixed quantities in traditional statistics which is not founded on Bayes’ theorem. Bayesian statistics therefore recommends itself for Monte Carlo methods, which generate random variates from given distributions. Monte Carlo methods, of course, can also be applied in traditional statistics. The unknown parameters, are introduced as functions of the measurements, and the Monte Carlo methods give the covariance matrix and the expectation of these functions. A confidence region is derived where the unknown parameters are situated with a given probability. Following a method of traditional statistics, hypotheses are tested by determining whether a value for an unknown parameter lies inside or outside the confidence region. The error propagation of a random vector by the Monte Carlo methods is presented as an application. If the random vector results from a nonlinearly transformed vector, its covariance matrix and its expectation follow from the Monte Carlo estimate. This saves a considerable amount of derivatives to be computed, and errors of the linearization are avoided. The Monte Carlo method is therefore efficient. If the functions of the measurements are given by a sum of two or more random vectors with different multivariate distributions, the resulting distribution is generally not known. The Monte Carlo methods are then needed to obtain the covariance matrix and the expectation of the sum. Numéro de notice : A2018-613 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jogs-2018-0003 Date de publication en ligne : 02/03/2018 En ligne : https://doi.org/10.1515/jogs-2018-0003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92646
in Journal of geodetic science > vol 8 n° 1 (January 2018) . - pp 18 - 29[article]Crop-rotation structured classification using multi-source sentinel images and LPIS for crop type mapping / Simon Bailly (2018)
Titre : Crop-rotation structured classification using multi-source sentinel images and LPIS for crop type mapping Type de document : Article/Communication Auteurs : Simon Bailly , Auteur ; Sébastien Giordano , Auteur ; Loïc Landrieu , Auteur ; Nesrine Chehata , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : 1-Pas de projet / Conférence : IGARSS 2018, IEEE International Geoscience And Remote Sensing Symposium, observing, understanding and forecasting the dynamics of our planet 22/07/2018 27/07/2018 Valencia Espagne Proceedings IEEE Importance : pp 1950 - 1953 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] image multitemporelle
[Termes IGN] image Sentinel-MSI
[Termes IGN] surveillance agricoleRésumé : (auteur) Automatic analysis of Sentinel image time series is recommended for monitoring agricultural land use in Europe. To improve classification capacities, we propose a temporal structured classification combining Sentinel images and former vintages of the Land-Parcel IdentAutomatic analysis of Sentinel image time series is recommended for monitoring agricultural land use in Europe. To improve classification capacities, we propose a temporal structured classification combining Sentinel images and former vintages of the Land-Parcel Identification System. Inter-annual crop rotations are learned and combined with the satellite images using a Conditional Random Field. The proposed methodology is tested on a 233 km2 study area located in France and with a 25 categories national nomenclature. The classification results are globally improved.ification System. Inter-annual crop rotations are learned and combined with the satellite images using a Conditional Random Field. The proposed methodology is tested on a 233 km2 study area located in France and with a 25 categories national nomenclature. The classification results are globally improved. Numéro de notice : C2018-054 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2018.8518427 Date de publication en ligne : 05/11/2018 En ligne : https://doi.org/10.1109/IGARSS.2018.8518427 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91343 Deep learning based vehicular mobility models for intelligent transportation systems / Jian Zhang (2018)
Titre : Deep learning based vehicular mobility models for intelligent transportation systems Type de document : Thèse/HDR Auteurs : Jian Zhang, Auteur ; Abdelkader El Kamel, Directeur de thèse Editeur : Lille [France] : Ecole Centrale de Lille Année de publication : 2018 Importance : 175 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée en vue d’obtenir le grade de Docteur, Spécialité : Automatique, génie informatique, traitement du signal et des images, Doctorat délivré par Centrale LilleLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de Markov
[Termes IGN] classification par réseau neuronal
[Termes IGN] données de flux
[Termes IGN] itération
[Termes IGN] mobilité humaine
[Termes IGN] modèle de simulation
[Termes IGN] sécurité routière
[Termes IGN] système de transport intelligent
[Termes IGN] trafic routier
[Termes IGN] transport
[Termes IGN] UML
[Termes IGN] véhicule sans piloteIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The intelligent transportation systems gain great research interests in recent years. Although the realistic traffic simulation plays an important role, it has not received enough attention. This thesis is devoted to studying the traffic simulation in microscopic level, and proposes corresponding vehicular mobility models. Using deep learning methods, these mobility models have been proven with a promising credibility to represent the vehicles in real-world. Firstly, a data-driven neural network based mobility model is proposed. This model comes from real-world trajectory data and allows mimicking local vehicle behaviors. By analyzing the performance of this basic learning based mobility model, we indicate that an improvement is possible and we propose its specification. An HMM is then introduced. The preparation of this integration is necessary, which includes an examination of traditional dynamics based mobility models and the adaptation method of “classical” models to our situation. At last, the enhanced model is presented, and a sophisticated scenario simulation is built with it to validate the theoretical results. The performance of our mobility model is promising and implementation issues have also been discussed. Note de contenu : 1- Introduction
2- Neural network based data-driven mobility model
3- Enhanced Mobility Model with HMM
4- Experiment platform and scenario simulation
Conclusions and PerspectivesNuméro de notice : 25873 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Automatique, génie informatique, traitement du signal et des images : École Centrale Lille : 2018 Organisme de stage : CRIStAL (laboratoire) nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02136219/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95562 Design and implementation of a model predictive observer for AHRS / Jafar Keighobadi in GPS solutions, vol 22 n° 1 (January 2018)
[article]
Titre : Design and implementation of a model predictive observer for AHRS Type de document : Article/Communication Auteurs : Jafar Keighobadi, Auteur ; Hamid Vosoughi, Auteur ; Javad Faraji, Auteur Année de publication : 2018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] angle d'Euler
[Termes IGN] attitude and heading reference system AHRS
[Termes IGN] erreur instrumentale
[Termes IGN] erreur systématique
[Termes IGN] estimateur
[Termes IGN] filtre de Kalman
[Termes IGN] GPS-INS
[Termes IGN] microsystème électromécanique
[Termes IGN] véhiculeRésumé : (auteur) A GPS-aided Inertial Navigation System (GAINS) is used to determine the orientation‚ position and velocity of ground and aerial vehicles. The data measured by Inertial Navigation System (INS) and GPS are commonly integrated through an Extended Kalman Filter (EKF). Since the EKF requires linearized models and complete knowledge of predefined stochastic noises‚ the estimation performance of this filter is attenuated by unmodeled nonlinearity and bias uncertainties of MEMS inertial sensors. The Attitude Heading Reference System (AHRS) is applied based on the quaternion and Euler angles methods. A moving horizon-based estimator such as Model Predictive Observer (MPO) enables us to approximate and estimate linear systems affected by unknown uncertainties. The main objective of this research is to present a new MPO method based on the duality principle between controller and observer of dynamic systems and its implementation in AHRS mode of a low-cost INS aided by a GPS. Asymptotic stability of the proposed MPO is proven by applying Lyapunov’s direct method. The field test of a GAINS is performed by a ground vehicle to assess the long-time performance of the MPO method compared with the EKF. Both the EKF and MPO estimators are applied in AHRS mode of the MEMS GAINS for the purpose of real-time performance comparison. Furthermore‚ we use flight test data of the GAINS for evaluation of the estimation filters. The proposed MPO based on both the Euler angles and quaternion methods yields better estimation performances compared to the classic EKF. Numéro de notice : A2018-017 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-017-0696-4 En ligne : https://doi.org/10.1007/s10291-017-0696-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89114
in GPS solutions > vol 22 n° 1 (January 2018)[article]
Titre : Estimation à la volée du vignettage par ajustement local du gain Type de document : Article/Communication Auteurs : Lâmân Lelégard , Auteur ; Manchun Lei , Auteur ; Bruno Vallet , Auteur ; Mathieu Brédif , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2018 Projets : 1-Pas de projet / Conférence : CFPT 2018, Conférence Française de Photogrammétrie et de Télédétection 25/06/2018 28/06/2018 Champs-sur-Marne France Open Access Proceedings Importance : 3 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] correction radiométrique
[Termes IGN] distorsion d'image
[Termes IGN] estimation statistique
[Termes IGN] image numériqueRésumé : (auteur) Les images acquises par tout système imageur présentent des distorsions radiométriques plus ou moins importantes se traduisant dans une grande majorité des cas par un assombrissement systématique en bord de champ, aussi appelé vignettage. Ce vignettage pose problème dès lors que l'on cherche à fusionner des images d’une même scène, que ce soit pour la texturation de modèles 3D, le rendu d’images panoramiques ou l’élaboration d’orthophotographies. Alors que ce vignettage est d'habitude estimé en laboratoire par une procédure de calibration radiométrique, on propose ici de l’estimer et de le corriger a posteriori, c'est à dire en utilisant uniquement des images acquises dans des conditions normales. Numéro de notice : C2018-007 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Poster nature-HAL : Poster-avec-CL DOI : sans Date de publication en ligne : 28/06/2018 En ligne : https://rfiap2018.ign.fr/programmeCFPT Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90340 Documents numériques
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