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Space–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning / Qisong Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)
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Titre : Space–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning Type de document : Article/Communication Auteurs : Qisong Wu, Auteur ; Yimin D. Zhang, Auteur ; Moeness G. Amin, Auteur ; Brahim Himed, Auteur Année de publication : 2016 Article en page(s) : pp 944 - 957 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage dirigé
[Termes IGN] capteur passif
[Termes IGN] estimation bayesienne
[Termes IGN] estimation des paramètres
[Termes IGN] filtre adaptatif
[Termes IGN] image radar
[Termes IGN] matrice de covarianceMots-clés libres : sparse Bayesian learning Résumé : (Auteur) Conventional space-time adaptive processing suffers from the requirement of a large number of secondary samples. In this paper, a novel method is proposed to accurately estimate the clutter covariance matrix based on a small number of secondary samples, by exploiting the common clutter support across nearby range cells in the angle-Doppler domain. By taking advantage of the intrinsic sparsity of the clutter in the angle-Doppler domain, the recently developed sparse Bayesian learning technique is employed for high-resolution clutter profile estimation. The proposed method does not require the independent and identically distributed secondary sample assumption, and the required number of secondary data samples can be significantly reduced. In addition, we propose a sparse reconstruction-based approach to acquire the 2-D motion parameters of moving targets, by exploiting their group sparsity in the velocity domain in the multistatic passive radar systems. Simulation results verify the effectiveness of the proposed algorithm. Numéro de notice : A2016-118 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2470518 En ligne : https://doi.org/10.1109/TGRS.2015.2470518 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79998
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 944 - 957[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016021 SL Revue Centre de documentation Revues en salle Disponible Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation / Göran Stahl in Forest ecosystems, vol 3 (2016)
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Titre : Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation Type de document : Article/Communication Auteurs : Göran Stahl, Auteur ; Svetlana Saarela, Auteur ; Sebastian Schnell, Auteur ; Sören Holm, Auteur ; et al., Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] échantillonnage
[Termes IGN] estimation statistique
[Termes IGN] inférence statistique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design-based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, model-based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters. Numéro de notice : A2016--161 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1186/s40663-016-006 En ligne : https://doi.org/10.1186/s40663-016-0064-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87015
in Forest ecosystems > vol 3 (2016)[article]A joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing / Chengjiang Long in International journal of computer vision, vol 116 n° 2 (15th January 2016)
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Titre : A joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing Type de document : Article/Communication Auteurs : Chengjiang Long, Auteur ; Gang Hua, Auteur ; Ashish Kapoor, Auteur Année de publication : 2016 Article en page(s) : pp 136 - 160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification bayesienne
[Termes IGN] classification dirigée
[Termes IGN] distribution de Gauss
[Termes IGN] inférence
[Termes IGN] production participative
[Termes IGN] reconnaissance d'objetsRésumé : (auteur) We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noise and the expertise level of each individual labeler with two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy for active selection of data samples to be labeled, and active selection of high quality labelers based on their estimated expertise to label the data. We apply the proposed model for four visual recognition tasks, i.e., object category recognition, multi-modal activity recognition, gender recognition, and fine-grained classification, on four datasets with real crowd-sourced labels from the Amazon Mechanical Turk. The experiments clearly demonstrate the efficacy of the proposed model. In addition, we extend the proposed model with the Predictive Active Set Selection Method to speed up the active learning system, whose efficacy is verified by conducting experiments on the first three datasets. The results show our extended model can not only preserve a higher accuracy, but also achieve a higher efficiency. Numéro de notice : A2016--137 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-015-0834-9 En ligne : https://doi.org/10.1007/s11263-015-0834-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85903
in International journal of computer vision > vol 116 n° 2 (15th January 2016) . - pp 136 - 160[article]
Titre : Advanced modeling and algorithms for high-precision GNSS analysis Type de document : Thèse/HDR Auteurs : Kan Wang, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2016 Collection : Dissertationen ETH num. 23188 Note générale : bibliographie
thesis submitted to attain the degree of doctor of sciences of ETH ZurichLangues : Anglais (eng) Descripteur : [Termes IGN] ambiguïté entière
[Termes IGN] antenne GPS
[Termes IGN] centre de phase
[Termes IGN] données BeiDou
[Termes IGN] données Galileo
[Termes IGN] données GPS
[Termes IGN] double différence
[Termes IGN] erreur systématique
[Termes IGN] GPS en mode différentiel
[Termes IGN] horloge
[Termes IGN] phase GNSS
[Termes IGN] positionnement cinématique
[Termes IGN] récepteur GNSS
[Termes IGN] récepteur trifréquence
[Termes IGN] résolution d'ambiguïté
[Termes IGN] retard ionosphèrique
[Termes IGN] Suisse
[Termes IGN] trajet multiple
[Vedettes matières IGN] Traitement de données GNSSRésumé : (auteur) In the recent ten years, the Global Navigation Satellite System (GNSS) processing has experienced a fast development in many areas including the increasing number of frequencies, the higher quality of positioning instruments, e.g. the receiver clocks and the satellite clocks, and more integrated modeling and calculation strategies. This thesis includes investigations of different modeling and parameterization methods in modern GNSS positioning with the focus on three important positioning error sources: the receiver clock errors, the phase ambiguities and the ionospheric delays.
The thesis shows that making use of the high-quality receiver clocks and applying appropriate receiver clock modeling can help to improve the kinematic height estimates, which are highly correlated with the receiver clock parameters. An efficient pre-elimination and back-substitution strategy of epoch parameters with relative clock constraints between subsequent and near-subsequent epochs has been developed to enable processing of, e.g., high-rate data. A detailed analysis of the relationship between the clock quality and the improvement of kinematic heights has been performed. Studies were also conducted to decorrelate the receiver clock parameters, the kinematic heights and the troposphere parameters. Experiments with real data have shown that appropriate deterministic and stochastic clock models can also be helpful to increase the resolution of the estimated Zenith Path Delay (ZPD) parameters without obvious degradation of the stability of the kinematic heights.
The second aspect of the thesis focuses on the resolution of triple-frequency phase ambiguities with different linear combinations. A complete analytical investigation of Geometry-Free (GF) and Ionosphere-Free (IF) triple-frequency phase ambiguity resolution with minimized noise level has been performed for different frequency triplets. The analysis was done separately for the best two linear combinations and the third one. Experiments have shown that the fractional parts and the formal errors of the combined ambiguities of the best two linear combinations are relatively small for Galileo E1, E5b and E5a and GPS L1, L2 and L5 triplets, while the third linear combination remains a challenge. Further analysis with the geostationary satellites of the Beidou Navigation Satellite System (BDS) elaborated in the framework of this thesis has also confirmed that the combined ambiguities from the best two GF and IF linear combinations can be fixed by rounding, while the estimated ambiguities on L1 have relatively large deviations from the values obtained from the traditional dual-frequency double-difference ambiguity resolution. Apart from the triple-frequency ambiguity resolution on the double-difference level, the so-called track-to-track ambiguities between different tracks of the same receiver and the same satellite have also been investigated for the best two triple-frequency linear combinations using GPS L1, L2 and L5 as well as Galileo E1, E5b and E5a observations. The outcome demonstrates that elevation-dependent influences on the observations like Phase Center Variations (PCVs), Phase Center Offsets (PCOs) and multipath are important for the fixing of the track-to-track ambiguities.
The combined track-to-track ambiguities using the best two linear combinations are also effective in detecting problems in the observation data.
The third aspect of the thesis includes the investigation of the differential ionospheric delays and gradients in the region of Switzerland from 1999 to 2013. In differential Global Positioning System (GPS) positioning, the ionospheric delays for short baselines are in most cases small enough to be ignored, except under extreme conditions, e.g., during ionospheric stormy days, and for applications with high integrity requirements, e.g., during approach and landing of aircrafts. This thesis introduces an algorithm using double-difference phase measurements with resolved phase ambiguities and global ionosphere maps provided by the Center for Orbit Determination in Europe (CODE) to extract the single-difference ionospheric delays, and enabling an automatic and robust processing of the data over 15 years. The results show that the daily maximum slant ionospheric gradients calculated from the differential slant ionopheric delays and the baseline lengths from 1999 to 2013 are below the slant ionosphere gradient boundary of the Conterminous United States (CONUS) ionospheric anomaly threat model.Numéro de notice : 17250 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse étrangère Note de thèse : dissertation : sciences : ETH Zurich : 2016 En ligne : http://dx.doi.org/10.3929/ethz-a-010610972 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81986 Application of topo-edaphic factors and remotely sensed vegetation indices to enhance biomass estimation in a heterogeneous landscape in the Eastern Arc mountains of Tanzania / Mercy Ojoyi in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)
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Titre : Application of topo-edaphic factors and remotely sensed vegetation indices to enhance biomass estimation in a heterogeneous landscape in the Eastern Arc mountains of Tanzania Type de document : Article/Communication Auteurs : Mercy Ojoyi, Auteur ; Onisimo Mutanga, Auteur ; John Olindi, Auteur ; Elfatih M. Abdel-Rahman, Auteur Année de publication : 2016 Article en page(s) : pp 1 - 21 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse
[Termes IGN] données topographiques
[Termes IGN] estimation statistique
[Termes IGN] facteur édaphique
[Termes IGN] forêt tropicale
[Termes IGN] image RapidEye
[Termes IGN] indice de végétation
[Termes IGN] montagne
[Termes IGN] surveillance écologique
[Termes IGN] TanzanieRésumé : (Auteur) Estimating tropical biomass is critical for establishment of conservation inventories and landscape monitoring. However, monitoring biomass in a complex and dynamic environment using traditional methods is challenging. Recently, biomass estimates based on remotely sensed data and ecological variables have shown great potential. The present study explored the utility of remotely sensed data and topo-edaphic factors to improve biomass estimation in the Eastern Arc Mountains of Tanzania. Twenty-nine vegetation indices were calculated from RapidEye data, while topo-edaphic factors were taken from field measurements. Results showed that using topo-edaphic variables or vegetation indices, biomass could be predicted with an R2 of 0.4. A combination of topo-edaphic variables and vegetation indices improved the prediction accuracy to an R2 of 0.6. Results further showed a decrease in biomass estimates from 1162 ton ha−1 in 1980 to 285.38 ton ha−1 in 2012. This study demonstrates the value of combining remotely sensed data with topo-edaphic variables in biomass estimation. Numéro de notice : A2016-079 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1041557 Date de publication en ligne : 20/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1041557 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79865
in Geocarto international > vol 31 n° 1 - 2 (January - February 2016) . - pp 1 - 21[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016011 RAB Revue Centre de documentation En réserve L003 Disponible PermalinkPermalinkCaractérisation des signaux et des bruits des séries temporelles du géocentre et des paramètres de rotation de la Terre (EOP) / Bachir Gourine in Bulletin des sciences géographiques, n° 30 (2015 - 2016)
PermalinkCombination of GNSS and SLR measurements : contribution to the realization of the terrestrial reference frame / Sara Bruni (2016)
PermalinkConvex programming approach to robust estimation of a multivariate Gaussian model / Samuel Balmand (2016)
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PermalinkA correctly weighted least squares adjustment - Part 1 Problems from using computed standard deviations / Charles D. Ghilani in xyHt, vol 2016 n° 1 (January 2016)
PermalinkPermalinkEléments de géodésie et de la théorie des moindres carrés / Abdelmajid Ben Hadj Salem (février 2016)
PermalinkEstimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)
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