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dc.contributor.authorOuassou, Mohammed
dc.contributor.authorKristiansen, Oddgeir
dc.contributor.authorGjevestad, Jon Glenn Omholt
dc.contributor.authorJacobsen, Knut Stanley
dc.contributor.authorAndalsvik, Yngvild Linnea
dc.date.accessioned2016-11-11T10:15:43Z
dc.date.accessioned2016-11-14T10:04:33Z
dc.date.available2016-11-11T10:15:43Z
dc.date.available2016-11-14T10:04:33Z
dc.date.issued2016
dc.identifier.citationInternational Journal of Navigation and Observation 2016, 2016:3582176:1-18nb_NO
dc.identifier.issn1687-5990
dc.identifier.urihttp://hdl.handle.net/11250/2420815
dc.description.abstractWe present a comparative study of computational methods for estimation of ionospheric scintillation indices. First, we review the conventional approaches based on Fourier transformation and low-pass/high-pass frequency filtration. Next, we introduce a novel method based on nonparametric local regression with bias Corrected Akaike Information Criteria (AICC). All methods are then applied to data from the Norwegian Regional Ionospheric Scintillation Network (NRISN), which is shown to be dominated by phase scintillation and not amplitude scintillation. We find that all methods provide highly correlated results, demonstrating the validity of the new approach to this problem. All methods are shown to be very sensitive to filter characteristics and the averaging interval. Finally, we find that the new method is more robust to discontinuous phase observations than conventional methods.nb_NO
dc.language.isoengnb_NO
dc.publisherHindawi Publishing Corporationnb_NO
dc.rightsNavngivelse 3.0 Norge*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/no/*
dc.titleEstimation of scintillation indices: a novel approach based on local kernel regression methodsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.date.updated2016-11-11T10:15:43Z
dc.source.journalInternational Journal of Navigation and Observationnb_NO
dc.identifier.doi10.1155/2016/3582176
dc.identifier.cristin1397826
dc.description.localcodeCopyright © 2016 Mohammed Ouassou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.nb_NO


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