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Improving the estimation of fractional-cycle biases for ambiguity resolution in precise point positioning / J. Geng in Journal of geodesy, vol 86 n° 8 (August 2012)  

Public [article]  inJournal of geodesy > vol 86 n° 8 (August 2012) . - pp 579 - 589 Titre : Improving the estimation of fractional-cycle biases for ambiguity resolution in precise point positioning Type de document : Article/Communication Auteurs : J. Geng, Auteur ; C. Shi, Auteur ; M. Ge, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 579 - 589 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] données GPS [Termes IGN] erreur systématique [Termes IGN] positionnement ponctuel précis [Termes IGN] résolution d'ambiguïté Résumé : (Auteur) Ambiguity resolution dedicated to a single global positioning system (GPS) station can improve the accuracy of precise point positioning. In this process, the estimation accuracy of the narrow-lane fractional-cycle biases (FCBs), which destroy the integer nature of undifferenced ambiguities, is crucial to the ambiguity-fixed positioning accuracy. In this study, we hence propose the improved narrow-lane FCBs derived from an ambiguity-fixed GPS network solution, rather than the original (i.e. previously proposed) FCBs derived from an ambiguity-float network solution. The improved FCBs outperform the original FCBs by ensuring that the resulting ambiguity-fixed daily positions coincide in nature with the state-of-the-art positions generated by the International GNSS Service (IGS). To verify this improvement, 1 year of GPS measurements from about 350 globally distributed stations were processed. We find that the original FCBs differ more from the improved FCBs when fewer stations are involved in the FCB estimation, especially when the number of stations is less than 20. Moreover, when comparing the ambiguity-fixed daily positions with the IGS weekly positions for 248 stations through a Helmert transformation, for the East component, we find that on 359 days of the year the daily RMS of the transformed residuals based on the improved FCBs is smaller by up to 0.8 mm than those based on the original FCBs, and the mean RMS over the year falls evidently from 2.6 to 2.2 mm. Meanwhile, when using the improved rather than the original FCBs, the RMS of the transformed residuals for the East component of 239 stations (i.e. 96.4% of all 248 stations) is clearly reduced by up to 1.6 mm, especially for stations located within a sparse GPS network. Therefore, we suggest that narrow-lane FCBs should be determined with ambiguity-fixed, rather than ambiguity-float, GPS network solutions. Numéro de notice : A2012-374 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-011-0537-0 Date de publication en ligne : 14/12/2011 En ligne : https://doi.org/10.1007/s00190-011-0537-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31820 [article]

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Total least squares adjustment in partial errors-in-variables models: algorithm and statistical analysis / P. Xu in Journal of geodesy, vol 86 n° 8 (August 2012)  

Public [article]  inJournal of geodesy > vol 86 n° 8 (August 2012) . - pp 661 - 675 Titre : Total least squares adjustment in partial errors-in-variables models: algorithm and statistical analysis Type de document : Article/Communication Auteurs : P. Xu, Auteur ; J. Liu, Auteur ; C. Shi, Auteur Année de publication : 2012 Article en page(s) : pp 661 - 675 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] analyse de variance [Termes IGN] compensation non linéaire [Termes IGN] compensation par moindres carrés [Termes IGN] modèle d'erreur [Termes IGN] résidu [Termes IGN] traitement de données GNSS Résumé : (Auteur) The weighted total least squares (TLS) method has been developed to deal with observation equations, which are functions of both unknown parameters of interest and other measured data contaminated with random errors. Such an observation model is well known as an errors-in-variables (EIV) model and almost always solved as a nonlinear equality-constrained adjustment problem. We reformulate it as a nonlinear adjustment model without constraints and further extend it to a partial EIV model, in which not all the elements of the design matrix are random. As a result, the total number of unknowns in the normal equations has been significantly reduced. We derive a set of formulae for algorithmic implementation to numerically estimate the unknown model parameters. Since little statistical results about the TLS estimator in the case of finite samples are available, we investigate the statistical consequences of nonlinearity on the nonlinear TLS estimate, including the first order approximation of accuracy, nonlinear confidence region and bias of the nonlinear TLS estimate, and use the bias-corrected residuals to estimate the variance of unit weight. Numéro de notice : A2012-378 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-012-0552-9 Date de publication en ligne : 23/03/2012 En ligne : https://doi.org/10.1007/s00190-012-0552-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31824 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
266-2012081	RAB	Revue	Centre de documentation	En réserve L003	Disponible