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An adaptive stochastic model for GPS observations and its performance in precise point positioning / J. Z. Zheng in Survey review, vol 48 n° 349 (July 2016)
[article]
Titre : An adaptive stochastic model for GPS observations and its performance in precise point positioning Type de document : Article/Communication Auteurs : J. Z. Zheng, Auteur ; F. Guo, Auteur Année de publication : 2016 Article en page(s) : pp 296 - 302 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] bruit (théorie du signal)
[Termes IGN] correction ionosphérique
[Termes IGN] fenêtre (informatique)
[Termes IGN] mesurage de pseudo-distance
[Termes IGN] modèle stochastique
[Termes IGN] positionnement par GPS
[Termes IGN] récepteur GPS
[Termes IGN] temps réelRésumé : (auteur) In this paper, the stochastic characteristics of Global Positioning System (GPS) pseudo-range noise as influenced by several factors such as receiver type, frequency, and ionosphere environment are analysed. The results indicate that the noise level of GPS observations is significantly affected by these factors. Moreover, the noise level is so mutable that it cannot be generalised and described by a uniform empirical model. Even for the same satellite, the noise level of observations may fluctuate sharply in both spatial and temporal resolution. To establish a reasonable stochastic model, a recursive sliding window method for estimating pseudo-range noise in real time is introduced. The effectiveness of the proposed method is verified by specific computational examples. Numéro de notice : A2016-626 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1179/1752270615Y.0000000033 En ligne : https://doi.org/10.1179/1752270615Y.0000000033 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81842
in Survey review > vol 48 n° 349 (July 2016) . - pp 296 - 302[article]Stochastic modeling of triple-frequency BeiDou signals: estimation, assessment and impact analysis / Bofeng Li in Journal of geodesy, vol 90 n° 7 (July 2016)
[article]
Titre : Stochastic modeling of triple-frequency BeiDou signals: estimation, assessment and impact analysis Type de document : Article/Communication Auteurs : Bofeng Li, Auteur Année de publication : 2016 Article en page(s) : pp 593 – 610 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] données BeiDou
[Termes IGN] erreur instrumentale
[Termes IGN] modèle stochastique
[Termes IGN] orbite géostationnaire
[Termes IGN] positionnement par GNSS
[Termes IGN] récepteur trifréquence
[Termes IGN] résolution d'ambiguïtéRésumé : (auteur) Stochastic models are important in global navigation satellite systems (GNSS) estimation problems. One can achieve reliable ambiguity resolution and precise positioning only by use of a suitable stochastic model. The BeiDou system has received increased research focus, but based only on empirical stochastic models from the knowledge of GPS. In this paper, we will systematically study the estimation, assessment and impacts of a triple-frequency BeiDou stochastic model. In our estimation problem, a single-difference, geometry-free functional model is used to extract pure random noise. A very sophisticated structure of unknown variance matrix is designed to allow the estimation of satellite-specific variances, cross correlations between two arbitrary frequencies, as well as the time correlations for phase and code observations per frequency. In assessing the stochastic models, six data sets with four brands of BeiDou receivers on short and zero-length baselines are processed, and the results are compared. In impact analysis of stochastic model, the performance of integer ambiguity resolution and positioning are numerically demonstrated using a realistic stochastic model. The results from ultrashort (shorter than 10 m) and zero-length baselines indicate that BeiDou stochastic models are affected by both observation and receiver brands. The observation variances have been modeled by an elevation-dependent function, but the modeling errors for geostationary earth orbit (GEO) satellites are larger than for inclined geosynchronous satellite orbit (IGSO) and medium earth orbit (MEO) satellites. The stochastic model is governed by both the internal errors of the receiver and external errors at the site. Different receivers have different capabilities for resisting external errors. A realistic stochastic model is very important for achieving ambiguity resolution with a high success rate and small false alarm and for determining realistic variances for position estimates. To the best of our knowledge, this paper is the first comprehensive study on such stochastic models used specifically with BeiDou data. Numéro de notice : A2016-424 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-016-0896-7 En ligne : http://dx.doi.org/10.1007/s00190-016-0896-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81318
in Journal of geodesy > vol 90 n° 7 (July 2016) . - pp 593 – 610[article]Markov random field-based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image / L. K. Tiwari in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)
[article]
Titre : Markov random field-based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image Type de document : Article/Communication Auteurs : L. K. Tiwari, Auteur ; S.K. Sinha, Auteur ; S. Saran, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 428 - 445 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] champ aléatoire de Markov
[Termes IGN] changement d'utilisation du sol
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] forêtRésumé : (Auteur) Forest encroachment (FE) is a problem in Andaman and Nicobar Islands (ANI) in India for environment and planning. Small gaps created in the forest slowly expand its periphery disturbing the biodiversity. Therefore, intrusion of poachers, slash and burn and other factors causing FE must be carefully detected and monitored. Remote sensing offers a great opportunity to accomplish this task because of its synoptic view. Conventional classification methods with remotely sensed images are problematic because of small size of FE and mixed landcover composition. This study presents an application of super-resolution mapping (SRM) based on Markov random field for detection of FE using ASTER (15 m) images. The SRM results were validated using multispectral IRS LISS-IV (5.8 m) image. Non-contiguous FE patches of various sizes and shapes are characterized using the spatial contextual information. The novelty of this approach lies in the identification and separability of small FE pockets which could not be achieved with pixel-based maximum likelihood classifier (MLC). The SRM parameters were optimized and found comparable to previous studies. Classification accuracy obtained with SRM at scale factor 3 is κ = 0.62 that is superior to accuracy of MLC (κ = 0.51). SRM is a promising tool for detection and monitoring of FE at Rutland Island in ANI, India. Numéro de notice : A2016-157 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1054441 Date de publication en ligne : 01/07/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1054441 Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80401
in Geocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 428 - 445[article]Réservation
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Titre : Introduction to Time Series and Forecasting Type de document : Monographie Auteurs : Peter J. Brockwell, Auteur ; Richard A. Davis, Auteur Editeur : Springer International Publishing Année de publication : 2016 Importance : 425 p. Format : 21 x 28 cm ISBN/ISSN/EAN : 978-3-319-29854-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse spectrale
[Termes IGN] calcul matriciel
[Termes IGN] matrice de covariance
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modèle de simulation
[Termes IGN] modèle stochastique
[Termes IGN] série temporelle
[Termes IGN] variable aléatoireRésumé : (éditeur) This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Many additional special topics are also covered. Note de contenu : 1- Introduction
2- Stationary Processes
3- ARMA Models
4- Spectral Analysis
5- Modeling and Forecasting with ARMA Processes
6- Nonstationary and Seasonal Time Series Models
7- Time Series Models for Financial Data
8- Multivariate Time Series
9- State-Space Models
10- Forecasting Techniques
11- Further TopicsNuméro de notice : 25750 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie En ligne : https://link.springer.com/book/10.1007%2F978-3-319-29854-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94942 Investigating efficacy of robust M-estimation of deformation from observation differences / Krzysztof Nowel in Survey review, vol 48 n° 346 (January 2016)
[article]
Titre : Investigating efficacy of robust M-estimation of deformation from observation differences Type de document : Article/Communication Auteurs : Krzysztof Nowel, Auteur Année de publication : 2016 Article en page(s) : pp 21 - 30 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] analyse de données
[Termes IGN] déformation géométrique
[Termes IGN] estimation statistique
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle stochastique
[Termes IGN] test de performanceRésumé : (auteur) In deformation analysis of geodetic control networks, it is important to know whether the estimated displacement value of a given point is the effect of displacement or only the effect of random measurement errors. The F-test of statistical significance is used to answer this question. This test is applied both in conventional deformation analysis (CDA) based on least square (LS) estimation and in deformation analysis based on robust M-estimation. Unfortunately, the F-test is strongly flawed in the latter case. As a consequence, its results are significantly different here than assumed/expected. This paper analyses how flawed the F-test is and proposes a new solution. First, the algorithm of the global and local F-tests was derived. It was then demonstrated that for deformation analysis based on robust M-estimation, this algorithm has theoretical flaws. Next, it was shown how these problems can be solved numerically. The basis of the solutions proposed involves the use of stochastic simulations and, more specifically, the Monte Carlo (MC) method. Moreover, it was noted that other statistical test problems which also occur in deformation analysis based on robust M-estimation can be solved using present-day computers. The numerical approach can be a good support here in the selection of the proper significance level as well as in the correct performance of the test sensitivity analysis. All theoretical discussions were verified on an example simulated control network. Numéro de notice : A2016-044 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/00396265.2015.1097585 En ligne : https://doi.org/10.1080/00396265.2015.1097585 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79637
in Survey review > vol 48 n° 346 (January 2016) . - pp 21 - 30[article]Modelling forest canopy trends with on-demand spatial simulation / Gordon M. Green in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)PermalinkPermalinkStochastic geometrical model and Monte Carlo optimization methods for building reconstruction from InSAR data / Yue Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)PermalinkUnsupervised segmentation of high-resolution remote sensing images based on classical models of the visual receptive field / Miaozhong Xu in Geocarto international, vol 30 n° 9 - 10 (October - November 2015)PermalinkA novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS / Abubrakr A. A. Al Sharif in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)PermalinkA probabilistic eco-hydrological model to predict the effects of climate change on natural vegetation at a regional scale / Jan-Philip M. Witte in Landscape ecology, vol 30 n° 5 (May 2015)PermalinkSpectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkSupervised spectral–spatial hyperspectral image classification with weighted markov random fields / Le Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkRegional gold potential mapping in Kelantan (Malaysia) using probabilistic based models and GIS / Suhaimizi Yusoff in Open geosciences, vol 7 n° 1 (January 2015)PermalinkEvaluation of feature-based 3-d registration of probabilistic volumetric scenes / Maria I. Restrepo in ISPRS Journal of photogrammetry and remote sensing, vol 98 (December 2014)PermalinkA hybrid framework for single tree detection from airborne laser scanning data: A case study in temperate mature coniferous forests in Ontario, Canada / Junjie Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 98 (December 2014)PermalinkSingle frequency GPS/Galileo precise point positioning using un-differenced and between-satellite single difference measurements / Akram Afifi in Geomatica, vol 68 n° 3 (September 2014)PermalinkAn intelligent approach towards automatic shape modelling and object extraction from satellite images using cellular automata based algorithm / P. V. Arun in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)PermalinkA class of cloud detection algorithms based on a MAP-MRF approach in space and time / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)PermalinkCloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model / Qing Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 92 (June 2014)PermalinkA probabilistic data model and algebra for location-based data warehouses and their implementation / Igor Timko in Geoinformatica, vol 18 n° 2 (April 2014)PermalinkComplexité algorithmique / Sylvain Perifel (2014)PermalinkContextual classification of lidar data and building object detection in urban areas / Joachim Niemeyer in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkFusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification / Markus Gerke in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkProbabilités pour les sciences de l'ingénieur / Manuel Samuelides (2014)Permalink