Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction
Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation t...
Saved in:
Published in | Sensors (Basel, Switzerland) Vol. 17; no. 3; p. 468 |
---|---|
Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
MDPI AG
27.02.2017
MDPI |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 10
electrons/m²) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area. |
---|---|
AbstractList | Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 10
electrons/m²) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area. Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 1016 electrons/m²) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area. Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 10 16 electrons/m 2 ) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area. Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 1016 electrons/m2) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area. |
Author | Geng, Jianghui Wang, Cheng Liu, Jingnan Zhang, Hongping Xu, Peiliang Huang, Ling |
AuthorAffiliation | 1 GNSS Research Center, Wuhan University, 129 Luoyu Road, 430079 Wuhan, China; huangling_gnss@whu.edu.cn (L.H.); jgeng@whu.edu.cn (J.G.); acheng@whu.edu.cn (C.W.); jnliu@whu.edu.cn (J.L.) 2 Disaster Prevention Research Institute, Kyoto University, 611-0011 Kyoto, Japan; pxu@rcep.dpri.kyoto-u.ac.jp |
AuthorAffiliation_xml | – name: 1 GNSS Research Center, Wuhan University, 129 Luoyu Road, 430079 Wuhan, China; huangling_gnss@whu.edu.cn (L.H.); jgeng@whu.edu.cn (J.G.); acheng@whu.edu.cn (C.W.); jnliu@whu.edu.cn (J.L.) – name: 2 Disaster Prevention Research Institute, Kyoto University, 611-0011 Kyoto, Japan; pxu@rcep.dpri.kyoto-u.ac.jp |
Author_xml | – sequence: 1 givenname: Ling surname: Huang fullname: Huang, Ling email: huangling_gnss@whu.edu.cn organization: GNSS Research Center, Wuhan University, 129 Luoyu Road, 430079 Wuhan, China. huangling_gnss@whu.edu.cn – sequence: 2 givenname: Hongping surname: Zhang fullname: Zhang, Hongping email: hpzhang@whu.edu.cn organization: GNSS Research Center, Wuhan University, 129 Luoyu Road, 430079 Wuhan, China. hpzhang@whu.edu.cn – sequence: 3 givenname: Peiliang surname: Xu fullname: Xu, Peiliang email: pxu@rcep.dpri.kyoto-u.ac.jp organization: Disaster Prevention Research Institute, Kyoto University, 611-0011 Kyoto, Japan. pxu@rcep.dpri.kyoto-u.ac.jp – sequence: 4 givenname: Jianghui surname: Geng fullname: Geng, Jianghui email: jgeng@whu.edu.cn organization: GNSS Research Center, Wuhan University, 129 Luoyu Road, 430079 Wuhan, China. jgeng@whu.edu.cn – sequence: 5 givenname: Cheng surname: Wang fullname: Wang, Cheng email: acheng@whu.edu.cn organization: GNSS Research Center, Wuhan University, 129 Luoyu Road, 430079 Wuhan, China. acheng@whu.edu.cn – sequence: 6 givenname: Jingnan surname: Liu fullname: Liu, Jingnan email: jnliu@whu.edu.cn organization: GNSS Research Center, Wuhan University, 129 Luoyu Road, 430079 Wuhan, China. jnliu@whu.edu.cn |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28264424$$D View this record in MEDLINE/PubMed |
BookMark | eNpdkUtvEzEUhS3UirYpC_4AGolNWaT4OfZskFAEJaISCLVsLT8nDhM72DOt-Pd1SIlaVrbuOfp8fM8ZOIopOgBeI3hJSAffF8QhgbQVL8ApopjOBcbw6Mn9BJyVsoYQE0LES3CCBW5pFU_B96859CH2zX0YV81t_BXTfWx-qhxUNK5ZpM22vhXH0viUmx-uDymqoVmmmMp25XIwdWhSLGOezFjFc3Ds1VDcq8dzBm4-f7pZfJlff7taLj5ezw1DZJxrrZTXkFpBjXZQCCtaJ7QXguMa2FqtLTGoVV3nqHWCKe1xazqrlfWYkRlY7rE2qbXc5rBR-Y9MKsi_g5R7qfIYzOAkZJA5zq3xDFLeVrDhuhOoY953Bu1YH_as7aQ3zpr63ayGZ9DnSgwr2ac7yQhnnNEKuHgE5PR7cmWUm1CMGwYVXZqKRIIzRCms25-Bt_9Z12nKdaVFYgRFKxgWO-C7vcvkVEp2_hAGQbmrXB4qr943T9MfnP86Jg8b_6os |
CitedBy_id | crossref_primary_10_1109_ACCESS_2020_3040546 crossref_primary_10_3390_rs14174286 crossref_primary_10_1088_1361_6501_aa89cc crossref_primary_10_1016_j_asr_2020_01_026 crossref_primary_10_1109_TGRS_2022_3218365 crossref_primary_10_1186_s40623_017_0762_8 crossref_primary_10_3390_atmos14091399 crossref_primary_10_1016_j_asr_2019_11_041 crossref_primary_10_3390_atmos13101626 crossref_primary_10_1007_s11600_020_00473_6 crossref_primary_10_3390_s17102298 crossref_primary_10_1029_2023SW003663 crossref_primary_10_1049_iet_rsn_2020_0202 crossref_primary_10_1088_1742_6596_1936_1_012012 crossref_primary_10_11728_cjss2020_04_505 crossref_primary_10_3390_rs14164125 crossref_primary_10_1109_TAP_2021_3111634 |
Cites_doi | 10.1080/10020070412331344711 10.1080/02693799008941549 10.2527/jas1985.611113x 10.1007/s00190-006-0032-1 10.1007/s00190-008-0226-9 10.1016/1352-2310(94)90477-4 10.5081/jgps.2.1.48 10.1016/j.geoderma.2011.02.010 10.1007/s00190-013-0638-z 10.1111/j.1365-2478.1994.tb00235.x 10.1007/BF02522340 10.1016/j.jastp.2005.07.017 10.1002/cjg2.1212 10.1002/joc.1583 10.1016/0047-259X(71)90001-7 10.1007/s00190-010-0404-4 10.1007/s10291-010-0174-8 10.2307/1938482 10.1556/AGeod.46.2011.1.6 10.1111/j.1365-246X.2009.04280.x 10.1016/S0022-1694(96)03250-7 10.1016/0273-1177(95)00927-2 10.1017/S0373463303002248 10.1016/j.asr.2012.06.026 10.1029/2007RS003786 10.1002/rds.20036 |
ContentType | Journal Article |
Copyright | 2017. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2017 by the authors. 2017 |
Copyright_xml | – notice: 2017. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2017 by the authors. 2017 |
DBID | NPM AAYXX CITATION 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PIMPY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
DOI | 10.3390/s17030468 |
DatabaseName | PubMed CrossRef ProQuest Central (Corporate) ProQuest - Health & Medical Complete保健、医学与药学数据库 ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials AUTh Library subscriptions: ProQuest Central ProQuest One Community College ProQuest Central Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
DatabaseTitle | PubMed CrossRef Publicly Available Content Database ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Central China ProQuest Hospital Collection (Alumni) ProQuest Central ProQuest Health & Medical Complete Health Research Premium Collection ProQuest Medical Library ProQuest One Academic UKI Edition Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest One Academic ProQuest Medical Library (Alumni) ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | PubMed MEDLINE - Academic Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: DOA name: Open Access: DOAJ - Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X7 name: ProQuest - Health & Medical Complete保健、医学与药学数据库 url: https://search.proquest.com/healthcomplete sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Engineering |
EISSN | 1424-8220 |
ExternalDocumentID | oai_doaj_org_article_0505e77dcf50476bbdc7b98195ff9c15 10_3390_s17030468 28264424 |
Genre | Journal Article |
GeographicLocations | China |
GeographicLocations_xml | – name: China |
GroupedDBID | --- 123 2WC 3V. 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH ABDBF ABJCF ABUWG ADBBV ADRAZ AENEX AFKRA AFZYC ALIPV ALMA_UNASSIGNED_HOLDINGS ARAPS BENPR BPHCQ BVXVI CCPQU CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE IPNFZ KB. KQ8 L6V M1P M48 M7S MODMG M~E NPM OK1 P2P P62 PDBOC PIMPY PQQKQ PROAC PSQYO RIG RNS RPM TUS UKHRP XSB ~8M AAYXX CITATION 7XB 8FK AZQEC DWQXO K9. PQEST PQUKI PRINS 7X8 5PM |
ID | FETCH-LOGICAL-c513t-bbaafb04d84cbe088d86e8bf8872220ddbbd3c16a99e4de85abf26c9dbadf253 |
IEDL.DBID | RPM |
ISSN | 1424-8220 |
IngestDate | Thu Jul 04 21:10:07 EDT 2024 Tue Sep 17 21:21:22 EDT 2024 Fri Jun 28 10:26:43 EDT 2024 Thu Oct 10 21:10:10 EDT 2024 Fri Aug 23 01:58:19 EDT 2024 Sat Sep 28 08:47:59 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 3 |
Keywords | semivariogram variance component estimation ionospheric delays CMONOC GNSS Kriging spatial interpolation |
Language | English |
License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c513t-bbaafb04d84cbe088d86e8bf8872220ddbbd3c16a99e4de85abf26c9dbadf253 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ORCID | 0000-0002-2603-1177 |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375754/ |
PMID | 28264424 |
PQID | 2108685284 |
PQPubID | 2032333 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_0505e77dcf50476bbdc7b98195ff9c15 pubmedcentral_primary_oai_pubmedcentral_nih_gov_5375754 proquest_miscellaneous_1875144033 proquest_journals_2108685284 crossref_primary_10_3390_s17030468 pubmed_primary_28264424 |
PublicationCentury | 2000 |
PublicationDate | 20170227 |
PublicationDateYYYYMMDD | 2017-02-27 |
PublicationDate_xml | – month: 2 year: 2017 text: 20170227 day: 27 |
PublicationDecade | 2010 |
PublicationPlace | Switzerland |
PublicationPlace_xml | – name: Switzerland – name: Basel |
PublicationTitle | Sensors (Basel, Switzerland) |
PublicationTitleAlternate | Sensors (Basel) |
PublicationYear | 2017 |
Publisher | MDPI AG MDPI |
Publisher_xml | – name: MDPI AG – name: MDPI |
References | ref13 ref35 ref12 ref15 ref37 ref14 ref36 ref11 ref33 ref2 ref17 ref39 ref16 ref19 ref18 Wang (ref25) 2006; 21 Komjathy (ref7) 2002 Li (ref23) 2011; 13 Helmert (ref31) 1907 Schaer (ref44) 1999 ref24 ref45 ref26 Cressie (ref30) 1993 Koch (ref34) 1999 Huang (ref43) 1992 ref20 Grafarend (ref32) 1980; 105 ref42 ref41 ref22 Sarma (ref10) 2010 ref21 ref28 ref27 ref29 ref8 ref9 ref3 Orús (ref1) 2005 ref5 ref40 Chao (ref6) 1997 Georgiadou (ref4) 1994 Sjöberg (ref38) 1983; 108 |
References_xml | – ident: ref5 doi: 10.1080/10020070412331344711 – year: 2005 ident: ref1 contributor: fullname: Orús – volume: 13 start-page: 659 year: 2011 ident: ref23 article-title: Ionospheric Delay Correction and Integrity Monitoring based on Kriging in GRIMS publication-title: Energy Procedia contributor: fullname: Li – ident: ref15 doi: 10.1080/02693799008941549 – ident: ref36 doi: 10.2527/jas1985.611113x – ident: ref29 – ident: ref35 doi: 10.1007/s00190-006-0032-1 – ident: ref42 doi: 10.1007/s00190-008-0226-9 – volume: 108 start-page: 382 year: 1983 ident: ref38 article-title: Unbiased estimation of variance-covariance components in condition adjustment with unknowns—A MINQUE approach publication-title: Z. Vermess. contributor: fullname: Sjöberg – ident: ref27 – volume: 105 start-page: 161 year: 1980 ident: ref32 article-title: An introduction to the variance-covariance component estimation of Helmert type publication-title: Z. Vermessungswes contributor: fullname: Grafarend – ident: ref16 doi: 10.1016/1352-2310(94)90477-4 – ident: ref20 doi: 10.5081/jgps.2.1.48 – ident: ref9 – ident: ref13 doi: 10.1016/j.geoderma.2011.02.010 – ident: ref18 doi: 10.1007/s00190-013-0638-z – volume: 21 start-page: 166 year: 2006 ident: ref25 article-title: Improved Kriging technique of ionospheric parameter foF2 instantaneous mapping publication-title: Chin. J. Radio Sci. contributor: fullname: Wang – ident: ref19 – year: 1999 ident: ref34 contributor: fullname: Koch – year: 1992 ident: ref43 contributor: fullname: Huang – year: 1999 ident: ref44 contributor: fullname: Schaer – ident: ref17 doi: 10.1111/j.1365-2478.1994.tb00235.x – year: 1994 ident: ref4 contributor: fullname: Georgiadou – ident: ref33 doi: 10.1007/BF02522340 – ident: ref21 doi: 10.1016/j.jastp.2005.07.017 – ident: ref26 doi: 10.1002/cjg2.1212 – ident: ref12 doi: 10.1002/joc.1583 – ident: ref37 doi: 10.1016/0047-259X(71)90001-7 – ident: ref2 doi: 10.1007/s00190-010-0404-4 – ident: ref3 doi: 10.1007/s10291-010-0174-8 – ident: ref14 doi: 10.2307/1938482 – ident: ref40 doi: 10.1556/AGeod.46.2011.1.6 – ident: ref39 doi: 10.1111/j.1365-246X.2009.04280.x – ident: ref11 doi: 10.1016/S0022-1694(96)03250-7 – year: 2010 ident: ref10 contributor: fullname: Sarma – year: 1993 ident: ref30 contributor: fullname: Cressie – ident: ref24 doi: 10.1016/0273-1177(95)00927-2 – ident: ref41 doi: 10.1017/S0373463303002248 – ident: ref45 doi: 10.1016/j.asr.2012.06.026 – year: 1997 ident: ref6 contributor: fullname: Chao – ident: ref8 – ident: ref22 doi: 10.1029/2007RS003786 – ident: ref28 doi: 10.1002/rds.20036 – year: 1907 ident: ref31 contributor: fullname: Helmert – year: 2002 ident: ref7 article-title: An assessment of the current WAAS algorithm in the South American region contributor: fullname: Komjathy |
SSID | ssj0023338 |
Score | 2.3638113 |
Snippet | Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for... |
SourceID | doaj pubmedcentral proquest crossref pubmed |
SourceType | Open Website Open Access Repository Aggregation Database Index Database |
StartPage | 468 |
SubjectTerms | CMONOC GNSS Harmonic functions Interpolation Ionosphere ionospheric delays Kriging interpolation Kriging spatial interpolation Mathematical models Navigation satellites Polynomials semivariogram Spherical caps Spherical harmonics Total Electron Content Variance variance component estimation |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LS8QwEA7iSQ_i2_oiiteyzatJjyrKqigirngrzWv10hV2_f_OtN11VwQvHtu0kM7XZObLJN8QciaZZzbzVVpoxVOpM5cakdmUq-jBHXJuFR4Uvn_I-wN5-6pe50p94Z6wVh64NVwPK60Frb2LKpM6t9Y7bQvM_sRYONaqlzI1JVMd1RLAvFodIQGkvjdmukkBmgXv04j0_xZZ_twgOedxrtfJWhcq0vO2ixtkKdSbZHVOQHCLPN41da2GFJdT6aDGFbKavgD_RTApDvZRjVslKMSm9CkMm4U_ejNChXA8-eco8s9vFdlt8nx99XzZT7saCalTTExSa6sq2kx6I50NMGV4kwdjI8wd4Pkz78FgwrG8KoogfTCqspHnrvC28pErsUOWa-jIHqHgzkyMtpGnkRaAiq4SOVwaL5mTLCGnU9OVH60SRgkMAu1bzuybkAs06uwBFK9ubgCkZQdp-RekCTmcQlJ2I2pcciwJZRR404SczJphLGCCo6rD6HNcMiBfmKwWIiG7LYKzngC1hNCPw9t6AduFri621O9vjd62EhqCWrn_H992QFY4BgZ4KF4fkmWANxxBWDOxx80f_AUMefkH priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Technology Collection dbid: 8FG link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3NT9VAEJ8oXvRgxA8oglmM14buV3d7ImB8okRjDBhuTffr6aUFH_z_zOzrKzxDPLbbJpOdne-d3wB8UDxwV4WubIwWpTKVL62sXCl0CmgOhXCaGoW_fa9PztXXC30xJtwW47XKlU7MijoMnnLkB4JGAlmN2vTw8qqkqVFUXR1HaDyGJ5yQ8KhTfPZ5Crgkxl9LNCGJof3BgptcCLRrNihD9T_kX_57TfKe3Zm9gOejw8iOlhzehEexfwnP7sEIvoIfp3m61ZxRUpWd95Qn69kvjIKJpYxEfujpwgRDD5X9jPOc_mNfBsIJp_4_zygKvcOSfQ1ns09nH0_KcVJC6TWX16VzXZdcpYJV3kVUHMHW0bqEGgTtfxWCc0F6XndNE1WIVncuido3wXUhCS3fwEaPhGwDQ6NmU3IZpEY5ZFfynazx0QbFveIFvF9tXXu5xMNoMY6g_W2n_S3gmDZ1-oAgrPOL4e-8HSWipRF60Zjgk66UqZFCb1xDZb2UGs91AbsrlrSjXC3au1NQwP60jBJBZY6uj8PNouUYglHJWsoCtpYcnCjBABMdQIF_mzXerpG6vtL_-Z1Rt7U06Nqqnf-T9RaeCjL81PRudmEDGRf30G25du_y2bwF99XwVA priority: 102 providerName: ProQuest – databaseName: Scholars Portal Journals: Open Access dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwdV3dT9RAEJ8gvuCD8QOwimYhvBa6X93tgzFqJAjBEMMR3pru12FienqHif73zvSulRJ8bLtpJjOzO_Pb2f0NwL7igbsiNHlltMiVKXxuZeFyoVPAcCiE03RR-OxLeTxRJ1f6ag36HpsrBS7uhXbUT2oy_37w--efdzjh3xLiRMh-uOCmK_DZB_BQKKnI0c_UUEwQEmHYklRoPJyIgC0lBEKNolJH3n9fxnn34OStSHT0BB6vUkj2fmnzp7AW22fw6Bax4HM4P-36XU0ZbbOySUs7Zy27RFxMRma0CMxaOkLBMGdlX-O02xBkn2fEHE43Aj0jXPqPXXYTLo4-XXw8zle9E3KvubzJnWua5AoVrPIu4lISbBmtS7imYEZQhOBckJ6XTVVFFaLVjUui9FVwTUhCyy1Yb1GQF8AwzNmUXEdboxwaMPlGlvhog-Je8Qz2etXVP5YMGTUiC1J1Pag6gw-k1GEAkVp3L2bzab2aIzU11YvGBJ90oUyJEnrjKir0pVR5rjPY6U1S945SC2oVZTVG2Qx2h884R6jw0bRx9mtRcwRlVMSWMoPtpQUHSXoPyMCMbDsSdfyl_Xbd8XBraTDZVS__-89XsCEoC6Ab8GYH1tFm8TXmMDfuTeehfwFIxPGS priority: 102 providerName: Scholars Portal |
Title | Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction |
URI | https://www.ncbi.nlm.nih.gov/pubmed/28264424 https://www.proquest.com/docview/2108685284 https://search.proquest.com/docview/1875144033 https://pubmed.ncbi.nlm.nih.gov/PMC5375754 https://doaj.org/article/0505e77dcf50476bbdc7b98195ff9c15 |
Volume | 17 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB615QIHxJuUsjKIa7qJH7FzpFWXAtpqVbVob1H82lai2Ypt_z8z3mTpIk5cLCVOpJFn7JnPHn8D8EmWvrSFb_NaK55LXbjciMLmXEWP7pBzq-ii8PSsOr2U3-ZqvgNquAuTkvadvT7sft4cdtdXKbfy9saNhzyx8Wx6rITGKEOOd2FXCzFA9B5lCQRdawohgXh-vCp1Ov2jwnwIL9D9c7nlgxJV_7_iy7_TJB_4nckzeNoHjOzzWrDnsBO6F_DkAY3gS5h9T9WtFow2VdllR_tkHfuBKJhUymjKLztKmGAYobLzsEjbf-zrknjC6f6fY4RC_3DJvoKLycnF8WneV0rInSrFXW5t20ZbSG-kswEXDm-qYGzEFQT9f-G9tV64smrrOkgfjGpt5JWrvW195Eq8hr0OBXkLDJ2aidEmkhppUV3RtaLCR-Nl6WSZwcdh6JrbNR9GgziChrrZDHUGRzSomw-Iwjq9WP5aNL0iGyqhF7T2LqpC6goldNrWdKwXY-1KlcHBoJKmn1erhlNhKKPQp2bwYdONM4KOOdouLO9XTYkQjI6shcjgzVqDG0kGC8hAb-l2S9TtHjTCxLrdG93-f__5Dh5zignoPrw-gD3UaXiPEc2dHaEdzzW2ZvJlBI-OTs5m56O0O4DtVJpRsvDfk6T-hA |
link.rule.ids | 230,315,733,786,790,870,891,2115,2236,12083,12792,21416,24346,27955,27956,31752,31753,33406,33407,33777,33778,43343,43633,43838,53825,53827,74100,74390,74657 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwEB7B9gAcEG_SFjCIa9Q4tmPnhFrUakvbVVVtUW9W_Fq4JIUt_78z2WzaRYhj4kQazXje9jcAnyUP3BWhyWutylzqwudGFC4vVQroDsvSKboofDarppfy25W6Ggpuy-FY5dom9oY6dJ5q5HsljQQyCq3pl-tfOU2Nou7qMELjIWwR5KaZwNbB4ez8Yky5BGZgKzwhgcn93pLrvhVoNrxQD9b_rwjz74OS9zzP0TN4OoSMbH8l4-fwILYv4Mk9IMGXcH7Sz7daMCqrssuWKmUt-455MAmVkdJ3LR2ZYBijsou46AuA7LgjpHC6AegZ5aF3aLKvYH50OP86zYdZCblXXNzkzjVNcoUMRnoX0XQEU0XjEtoQjACKEJwLwvOqqesoQzSqcamsfB1cE1KpxGuYtEjIW2Do1kxKroepkQ4FlnwjKnw0QXIveQaf1qyz1ytEDIuZBPHXjvzN4ICYOn5AINb9i-73wg46YWmIXtQ6-KQKqSuk0GtXU2MvpdpzlcHuWiR20KylvdsHGXwcl1EnqNHRtLH7s7QckzBqWguRwZuVBEdKMMXEELDEv_WGbDdI3Vxpf_7ocbeV0Bjcyu3_k_UBHk3nZ6f29Hh2sgOPSwoD6Aq83oUJCjG-wyDmxr0fduotfS30qw |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwEB5BkRAcKt6kFDCIa7SJH7FzQtCytBSqCrWoNyt-LVySwpb_z4w3m3YR4pg4kUYez_gbz_gbgDeyDrWrQle2WvFS6sqXRlSu5CoF3A45d4ouCn85bg7O5KdzdT7WPy3Hssq1T8yOOgyezshnnFoCGYXedJbGsoiT_fnbi58ldZCiTOvYTuMm3CKQTW0czPzjFHwJjMVWzEICB2fLWuekoNnYjzJt_7-w5t8lk9f2oPk92B7BI3u30vZ9uBH7B3D3GqXgQzg5yp2uFowOWNlZT2dmPfuGETGpl5H5Dz0VTzBEq-xrXOSjQHY4EGc43QX0jCLSK17ZR3A6_3C6d1COXRNKr2pxWTrXdclVMhjpXUQnEkwTjUvoTRALVCE4F4Svm65towzRqM4l3vg2uC4krsRj2OpRkKfAcIMzKblMWCMdqi75TjT4aIKsvawLeL2eOnux4sawGFPQ_Nppfgt4T5M6fUB01vnF8GthR-uw1E4vah18UpXUDUrotWspxZdS62tVwO5aJXa0saW9WhEFvJqG0Too5dH1cfi9tDWGY5S-FqKAJysNTpJgsIlgkOPfekO3G6JujvQ_vmcGbiU0wly583-xXsJtXKL28-Hx0TO4wwkP0F14vQtbqMP4HNHMpXuRl-kfd633cQ |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Kriging+with+Unknown+Variance+Components+for+Regional+Ionospheric+Reconstruction&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Huang%2C+Ling&rft.au=Zhang%2C+Hongping&rft.au=Xu%2C+Peiliang&rft.au=Geng%2C+Jianghui&rft.date=2017-02-27&rft.eissn=1424-8220&rft.volume=17&rft.issue=3&rft_id=info:doi/10.3390%2Fs17030468&rft_id=info%3Apmid%2F28264424&rft.externalDocID=28264424 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |