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A near-real-time automatic orbit determination system for COSMIC and its follow-on satellite mission: analysis of orbit and clock errors on radio occultation / Yi-Shan Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 1 (June 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 1 (June 2014) . - pp 3192 - 3210 Titre : A near-real-time automatic orbit determination system for COSMIC and its follow-on satellite mission: analysis of orbit and clock errors on radio occultation Type de document : Article/Communication Auteurs : Yi-Shan Li, Auteur ; Cheinway Hwang, Auteur ; Tzu-Pang Tseng, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 3192 - 3210 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Technologies spatiales [Termes IGN] mécanique orbitale [Termes IGN] mission spatiale [Termes IGN] orbitographie [Termes IGN] temps réel [Termes IGN] trajectographie (positionnement) Résumé : (Auteur) The COSMIC-2 mission is a follow-on mission of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) with an upgraded payload for improved radio occultation (RO) applications. The objective of this paper is to develop a near-real-time (NRT) orbit determination system, called NRT National Chiao Tung University (NCTU) system, to support COSMIC-2 in atmospheric applications and verify the orbit product of COSMIC. The system is capable of automatic determinations of the NRT GPS clocks and LEO orbit and clock. To assess the NRT (NCTU) system, we use eight days of COSMIC data (March 24-31, 2011), which contain a total of 331 GPS observation sessions and 12 393 RO observable files. The parallel scheduling for independent GPS and LEO estimations and automatic time matching improves the computational efficiency by 64% compared to the sequential scheduling. Orbit difference analyses suggest a 10-cm accuracy for the COSMIC orbits from the NRT (NCTU) system, and it is consistent as the NRT University Corporation for Atmospheric Research (URCA) system. The mean velocity accuracy from the NRT orbits of COSMIC is 0.168 mm/s, corresponding to an error of about 0.051 urad in the bending angle. The rms differences in the NRT COSMIC clock and in GPS clocks between the NRT (NCTU) and the postprocessing products are 3.742 and 1.427 ns. The GPS clocks determined from a partial ground GPS network [from NRT (NCTU)] and a full one [from NRT (UCAR)] result in mean rms frequency stabilities of 6.1E-12 and 2.7E-12, respectively, corresponding to range fluctuations of 5.5 and 2.4 cm and bending angle errors of 3.75 and 1.66 urad . Numéro de notice : A2014-307 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2271547 En ligne : https://doi.org/10.1109/TGRS.2013.2271547 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33210 [article]

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065-2014061A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Subspace matching pursuit for sparse unmixing of hyperspectral data / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 1 (June 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 1 (June 2014) . - pp 3256 - 3274 Titre : Subspace matching pursuit for sparse unmixing of hyperspectral data Type de document : Article/Communication Auteurs : Zhenwei Shi, Auteur ; Wei Tang, Auteur ; Zhana Duren, Auteur ; Zhiguo Jiang, Auteur Année de publication : 2014 Article en page(s) : pp 3256 - 3274 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] appariement d'images [Termes IGN] image hyperspectrale [Termes IGN] vecteur aléatoire multidimensionnel Résumé : (Auteur) Sparse unmixing assumes that each mixed pixel in the hyperspectral image can be expressed as a linear combination of only a few spectra (endmembers) in a spectral library, known a priori. It then aims at estimating the fractional abundances of these endmembers in the scene. Unfortunately, because of the usually high correlation of the spectral library, the sparse unmixing problem still remains a great challenge. Moreover, most related work focuses on the l1 convex relaxation methods, and little attention has been paid to the use of simultaneous sparse representation via greedy algorithms (GAs) (SGA) for sparse unmixing. SGA has advantages such as that it can get an approximate solution for the l0 problem directly without smoothing the penalty term in a low computational complexity as well as exploit the spatial information of the hyperspectral data. Thus, it is necessary to explore the potential of using such algorithms for sparse unmixing. Inspired by the existing SGA methods, this paper presents a novel GA termed subspace matching pursuit (SMP) for sparse unmixing of hyperspectral data. SMP makes use of the low-degree mixed pixels in the hyperspectral image to iteratively find a subspace to reconstruct the hyperspectral data. It is proved that, under certain conditions, SMP can recover the optimal endmembers from the spectral library. Moreover, SMP can serve as a dictionary pruning algorithm. Thus, it can boost other sparse unmixing algorithms, making them more accurate and time efficient. Experimental results on both synthetic and real data demonstrate the efficacy of the proposed algorithm. Numéro de notice : A2014-308 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2272076 En ligne : https://doi.org/10.1109/TGRS.2013.2272076 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33211 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014061A	RAB	Revue	Centre de documentation	En réserve L003	Disponible