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Auteur U.M. Durdag |
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Outlier detection by using fault detection and isolation techniques in geodetic networks / U.M. Durdag in Survey review, vol 48 n° 351 (October 2016)
[article]
Titre : Outlier detection by using fault detection and isolation techniques in geodetic networks Type de document : Article/Communication Auteurs : U.M. Durdag, Auteur ; S. Hekimoglu, Auteur ; B. Erdogan, Auteur Année de publication : 2016 Article en page(s) : pp 400 - 408 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] coordonnées GNSS
[Termes IGN] décomposition
[Termes IGN] données GNSS
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] système de référence géodésique
[Termes IGN] valeur aberranteRésumé : (Auteur) Fault detection and isolation (FDI) techniques, which are called standard parity space approach (SPSA) and optimal parity vector approach (OPVA), have been presented in literature extensively for engineering sensor systems or sensor networks. This paper demonstrates the abilities of these approaches to detect and isolate outliers in geodetic networks. The ability to detect and isolate outliers has been measured by computing the mean success rate (MSR) for some given probability of significance levels. These approaches have been applied to a levelling network and a Global Navigation Satellite System (GNSS) network. Different matrix decomposition techniques have been used as an alternative way to the Potter algorithm, which is used in SPSA and OPVA. It has been proven that the abilities of FDI techniques, i.e. the MSRs of OPVA, increase with regard to the ones of SPSA in the levelling network and the GNSS network especially if the significance level α is chosen as 0.001 by using Monte–Carlo simulation. Numéro de notice : A2016-822 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1179/1752270615Y.0000000038 En ligne : https://doi.org/10.1179/1752270615Y.0000000038 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82638
in Survey review > vol 48 n° 351 (October 2016) . - pp 400 - 408[article]