Show simple item record

dc.contributor.authorNorberg, J.
dc.contributor.authorVierinen, Juha
dc.contributor.authorRoininen, L
dc.contributor.authorOrispää, M.
dc.contributor.authorKauristie, K
dc.contributor.authorRideout, W.
dc.contributor.authorCoster, A. J.
dc.contributor.authorLehtinen, M
dc.date.accessioned2019-05-27T13:23:15Z
dc.date.available2019-05-27T13:23:15Z
dc.date.issued2018-08-22
dc.description.abstractIn ionospheric tomography, the atmospheric electron density is reconstructed from different electron density related measurements, most often from ground-based measurements of satellite signals. Typically, ionospheric tomography suffers from two major complications. First, the information provided by measurements is insufficient and additional information is required to obtain a unique solution. Second, with necessary spatial and temporal resolutions, the problem becomes very high dimensional, and hence, computationally infeasible. With Bayesian framework, the required additional information can be given with prior probability distributions. The approach then provides physically quantifiable probabilistic interpretation for all model variables. Here, Gaussian Markov random fields (GMRFs) are used for constructing the prior electron density distribution. The use of GMRF introduces sparsity to the linear system, making the problem computationally feasible. The method is demonstrated over Fennoscandia with measurements from global navigation satellite system (GNSS) and low Earth orbit (LEO) satellite receiver networks, GNSS occultation receivers, LEO satellite Langmuir probes, and ionosonde and incoherent scatter radar measurements.en_US
dc.descriptionSource at <a href=https://doi.org/10.1109/TGRS.2018.2847026>https://doi.org/10.1109/TGRS.2018.2847026</a>.en_US
dc.identifier.citationNorberg, J., Vierinen, J., Roininen, L., Orispää, M., Kauristie, K., Rideout, W.C., Coster, A.J. & Lehtinen M. (2018). Gaussian Markov random field priors in ionospheric 3D multi-instrument tomography. <i>IEEE Transactions on Geoscience and Remote Sensing, 56</i>(12), 7009-7021. https://doi.org/10.1109/TGRS.2018.2847026en_US
dc.identifier.cristinIDFRIDAID 1586178
dc.identifier.doi10.1109/TGRS.2018.2847026
dc.identifier.issn0196-2892
dc.identifier.issn1558-0644
dc.identifier.urihttps://hdl.handle.net/10037/15387
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE Transactions on Geoscience and Remote Sensing
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Mathematics and natural science: 400::Geosciences: 450::Other geosciences: 469en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Andre geofag: 469en_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430::Astrophysics, astronomy: 438en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430::Astrofysikk, astronomi: 438en_US
dc.subjectBayesianen_US
dc.subjectGaussian Markov random fields (GMRFs)en_US
dc.subjectIonospheric tomographyen_US
dc.subjectMulti-instrumenten_US
dc.titleGaussian Markov random field priors in ionospheric 3D multi-instrument tomographyen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


File(s) in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record