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Modeling long-period noise in kinematic GPS applications / A. Borsa in Journal of geodesy, vol 81 n° 2 (February 2007)  

Public [article]  inJournal of geodesy > vol 81 n° 2 (February 2007) . - pp 157 - 170 Titre : Modeling long-period noise in kinematic GPS applications Type de document : Article/Communication Auteurs : A. Borsa, Auteur ; J.B. Minster, Auteur ; B.G. Bills, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 157 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] Bolivie [Termes IGN] bruit (théorie du signal) [Termes IGN] désert [Termes IGN] erreur moyenne quadratique altimétrique [Termes IGN] filtrage du bruit [Termes IGN] GPS en mode cinématique [Termes IGN] problème inverse Résumé : (Auteur) We develop and test an algorithm for modeling and removing elevation error in kinematic GPS trajectories in the context of a kinematic GPS survey of the salar de Uyuni, Bolivia. Noise in the kinematic trajectory ranges over 15 cm and is highly autocorrelated, resulting in significant contamination of the topographic signal. We solve for a noise model using crossover differences at trajectory intersections as constraints in a least-squares inversion. Validation of the model using multiple realizations of synthetic/simulated noise shows an average decrease in root-mean-square-error (RMSE) by a factor of four. Applying the model to data from the salar de Uyuni survey, we find that crossover differences drop by a factor of eight (from an RMSE of 5.6 to 0.7 cm), and previously obscured topographic features are revealed in a plan view of the corrected trajectory. We believe that this algorithm can be successfully adapted to other survey methods that employ kinematic GPS for positioning. Copyright Springer Numéro de notice : A2007-049 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-006-0097-x En ligne : https://doi.org/10.1007/s00190-006-0097-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28414 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
266-07021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
266-07022	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Decomposition of laser altimeter waveforms / M.A. Hofton in IEEE Transactions on geoscience and remote sensing, vol 38 n° 4 Tome 2 (july 2000)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 38 n° 4 Tome 2 (july 2000) . - pp 1989 - 1996 Titre : Decomposition of laser altimeter waveforms Type de document : Article/Communication Auteurs : M.A. Hofton, Auteur ; J.B. Minster, Auteur ; J.B. Blair, Auteur Année de publication : 2000 Article en page(s) : pp 1989 - 1996 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] approximation [Termes IGN] Californie (Etats-Unis) [Termes IGN] décomposition de Gauss [Termes IGN] données laser [Termes IGN] données localisées 3D [Termes IGN] forêt [Termes IGN] lasergrammétrie [Termes IGN] onde [Termes IGN] optimisation (mathématiques) Résumé : (Auteur) We develop a method to decompose a laser altimeter return waveform into a series of components assuming that the position of each component within the waveform can be used to calculate the mean elevation of a specific reflecting surface within the laser footprint. For simplicity, they assume each component is Gaussian in nature. They estimate the number of Gaussian components from the number of inflection points of a smoothed copy of the laser waveform and obtain initial estimates of the Gaussian half-widths and positions from the positions of its consecutive inflection points. Initial amplitude estimates are obtained using a nonnegative least-squares method (LSM). To reduce the likelihood of fitting the background noise within the waveform and to minimize the number of Gaussians needed in the approximation, we rank the “importance” of each Gaussian in the decomposition using its initial half-width and amplitude estimates. The initial parameter estimates of all Gaussians ranked “important” are optimized using the Levenburg-Marquardt method. If the sum of the Gaussians does not approximate the return waveform to a prescribed accuracy, then additional Gaussians can be included in the optimization procedure or initial parameters can be recalculated. The Gaussian decomposition method is demonstrated on data collected by the airborne laser vegetation imaging sensor (LVIS) in October 1997 over the Sequoia National Forest, California. Copyright IEEE Numéro de notice : A2000-273 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/36.851780 En ligne : https://doi.org/10.1109/36.851780 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26397 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-00041B	RAB	Revue	Centre de documentation	En réserve L003	Disponible