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Modeling multifrequency GPS multipath fading in land vehicle environments / Vicente Carvalho Lima Filho in GPS solutions, vol 25 n° 1 (January 2021)  

Public [article]  inGPS solutions > vol 25 n° 1 (January 2021) . - 14 p. Titre : Modeling multifrequency GPS multipath fading in land vehicle environments Type de document : Article/Communication Auteurs : Vicente Carvalho Lima Filho, Auteur ; Alison Moraes, Auteur Année de publication : 2021 Article en page(s) : 14 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] densité de probabilité [Termes IGN] interférence [Termes IGN] propagation du signal [Termes IGN] qualité du signal [Termes IGN] signal GPS [Termes IGN] simulation de signal [Termes IGN] trajet multiple [Termes IGN] véhicule Résumé : (auteur) The reliability and performance of GPS receivers depend on the quality of the signal received, which can be largely affected by the interference caused by buildings, trees, and other obstacles. Since obstacles are always present in practical applications, several statistical representations have been developed along the years to measure, predict, and compensate errors induced by interferences. Two of the most used models to characterize GPS signal fading are the Nakagami-m and Rice, but in this work, we present evidence that supports the κ–μ distribution as the best fit to deal with multifrequency GPS multipath channels inside urban, rural, and forest areas. A synthetic signal simulator was developed to create propagation cases involving scattering clusters and specular reflections. Additionally, experimental measurements are presented to confirm the κ–μ distribution as the best distribution to characterize different situations on the available three GPS frequencies. We then present typical values of fading coefficients in L1, L2C, and L5 signals, for cases involving urban canyons, regular urban, rural, and dense vegetation areas. These coefficients can also be used to evaluate the receiver performance under similar cases or may be applied in weights measurement methods for positioning computation improvement. Numéro de notice : A2021-002 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-020-01040-8 Date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1007/s10291-020-01040-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96080 [article]

Probability and statistics / Arak M. Mathai (2018)  

Public Titre : Probability and statistics : A course for physicists and engineers Type de document : Guide/Manuel Auteurs : Arak M. Mathai, Auteur ; Hans J. Haubold, Auteur Editeur : Berlin, New York : Walter de Gruyter Année de publication : 2018 Importance : 582 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-3-11-056253-8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] densité de probabilité [Termes IGN] distribution, loi de [Termes IGN] échantillonnage (statistique) [Termes IGN] estimation statistique [Termes IGN] régression [Termes IGN] théorie des probabilités [Termes IGN] variable aléatoire Résumé : (éditeur) This textbook offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, as well as basics of sampling distributions, estimation and hypothesis testing. As the basis for courses on space and atmospheric science, remote sensing, geographic information systems, meteorology, climate and satellite communications at UN-affiliated regional centers, various applications of the formal theory are discussed as well. These include applied topics such as model building and experiment design. Designed for students in engineering and physics with applications in mind. Note de contenu : Introduction 1- Random phenomena 2- Probability 3- Random variables 4- Expected values 5- Commonly used density functions 6- Commonly used density functions 7- Commonly used density functions 8- Some multivariate distributions 9- Collection of random variables 10- Sampling distributions 11- Estimation 12- Interval estimation 13- Tests of statistical hypotheses 14- Model building and regression 15- Design of experiments and analysis of variance 16- Questions and answers Numéro de notice : 25970 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Manuel de cours DOI : 10.1515/9783110562545 En ligne : https://doi.org/10.1515/9783110562545 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96610

Automatic parameter selection for intensity-based registration of imagery to LiDAR data / Ebadat Ghanbari Parmehr in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7032 - 7043 Titre : Automatic parameter selection for intensity-based registration of imagery to LiDAR data Type de document : Article/Communication Auteurs : Ebadat Ghanbari Parmehr, Auteur ; Clive Simpson Fraser, Auteur ; Chunsun Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 7032 - 7043 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] appariement d'images [Termes IGN] appariement de données localisées [Termes IGN] densité de probabilité [Termes IGN] données lidar [Termes IGN] image aérienne [Termes IGN] image binaire [Termes IGN] segmentation binaire [Termes IGN] semis de points Résumé : (Auteur) Automatic registration of multisensor data, for example, imagery and Light Detection And Ranging (LiDAR), is a basic step in data fusion in the field of geospatial information processing. Mutual information (MI) has recently attracted research attention as a statistical similarity measure for intensity-based registration of multisensor images in the related fields of computer vision and remote sensing. Since MI-based registration methods rely on joint probability density functions (pdfs) for the data sets, errors in pdf estimation can affect the MI value, causing registration failure due to the presence of nonmonotonic surfaces of similarity measure. The quality of the estimated pdf is highly dependent upon both the bin size and the smoothing technique used in the pdf estimation procedure. The lack of a general approach to assign an appropriate bin size value for the pdf of multisensor data reduces both the level of automation and the robustness of the registration. In this paper, a novel bin size selection approach is proposed to improve registration reliability. The proposed method determines the best (uniform or variable) bin size for the pdf estimation via an analysis of the relationship between the similarity measure values of the data and the adopted geometric transformation. This highlights the role of the component of MI sensitive to the transformation, rather than the MI component that is unrelated to the transformation, such as noise. The performance of the proposed method for the registration of aerial imagery to LiDAR point clouds is investigated, and experimental results are compared with those achieved through a feature-based registration method. Numéro de notice : A2016-923 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2594294 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2594294 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83327 [article]

Systematic effects in laser scanning and visualization by confidence regions / Karl Rudolf Koch in Journal of applied geodesy, vol 10 n° 4 (December 2016)  

Public [article]  inJournal of applied geodesy > vol 10 n° 4 (December 2016) . - pp 247 – 257 Titre : Systematic effects in laser scanning and visualization by confidence regions Type de document : Article/Communication Auteurs : Karl Rudolf Koch, Auteur ; Jan Martin Brockmann, Auteur Année de publication : 2016 Article en page(s) : pp 247 – 257 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] carte de confiance [Termes IGN] covariance [Termes IGN] densité de probabilité [Termes IGN] distribution de Gauss [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] ellipsoïde (géodésie) [Termes IGN] itération [Termes IGN] matrice de covariance [Termes IGN] mesure géométrique [Termes IGN] méthode de Monte-Carlo [Termes IGN] série temporelle [Termes IGN] visualisation Résumé : (auteur) A new method for dealing with systematic effects in laser scanning and visualizing them by confidence regions is derived. The standard deviations of the systematic effects are obtained by repeatedly measuring three-dimensional coordinates by the laser scanner. In addition, autocovariance and cross-covariance functions are computed by the repeated measurements and give the correlations of the systematic effects. The normal distribution for the measurements and the multivariate uniform distribution for the systematic effects are applied to generate random variates for the measurements and random variates for the measurements plus systematic effects. Monte Carlo estimates of the expectations and the covariance matrix of the measurements with systematic effects are computed. The densities for the confidence ellipsoid for the measurements and the confidence region for the measurements with systematic effects are obtained by relative frequencies. They only depend on the size of the rectangular volume elements for which the densities are determined. The problem of sorting the densities is solved by sorting distances together with the densities. This allows a visualization of the confidence ellipsoid for the measurements and the confidence region for the measurements with systematic effects. Numéro de notice : A2016-975 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/jag-2016-0012 En ligne : https://doi.org/10.1515/jag-2016-0012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83682 [article]

A 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5100 - 5115 Titre : A class of cloud detection algorithms based on a MAP-MRF approach in space and time Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Paolo Adesso, Auteur ; Maurizio Longo, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5100 - 5115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] champ aléatoire de Markov [Termes IGN] classification [Termes IGN] corrélation croisée maximale [Termes IGN] densité de probabilité [Termes IGN] détection des nuages Résumé : (Auteur) A recurrent concern in cloud detection approaches is the high misclassification rate for pixels close to cloud edges. We tackle this problem by introducing a novel penalty term within the classical maximum a posteriori probability-Markov random field (MAP-MRF) approach. To improve the classification rate, such term, for which we suggest two different functional forms, accounts for the predictable motion of cloud volumes across images. Two mass tracking techniques are proposed. The first one is an effective and efficient implementation of the probability hypothesis density (PHD) filter, which is based on Gaussian mixtures (GMs) and relies on finite set statistics (FISST). The second one is a region matching procedure based on a maximum cross-correlation (MCC) that is characterized by low computational load. Through extensive tests on simulated images and real data, acquired by the SEVIRI sensor, both methods show a clear performance gain in comparison with classical spatial MRF-based algorithms. Numéro de notice : A2014-435 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2286834 En ligne : https://doi.org/10.1109/TGRS.2013.2286834 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73972 [article]

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