Hyppää sisältöön
    • FI
    • ENG
  • FI
  • /
  • EN
OuluREPO – Oulun yliopiston julkaisuarkisto / University of Oulu repository
Näytä viite 
  •   OuluREPO etusivu
  • Oulun yliopisto
  • Avoin saatavuus
  • Näytä viite
  •   OuluREPO etusivu
  • Oulun yliopisto
  • Avoin saatavuus
  • Näytä viite
JavaScript is disabled for your browser. Some features of this site may not work without it.

Gaussian Markov random field priors in ionospheric 3-D multi-instrument tomography

Norberg, Johannes; Vierinen, Juha; Roininen, Lassi; Orispää, Mikko; Kauristie, Kirsti; Rideout, William C.; Coster, Anthea J.; Lehtinen, Markku S. (2018-08-22)

 
Avaa tiedosto
nbnfi-fe2018121751126.pdf (12.84Mt)
nbnfi-fe2018121751126_meta.xml (43.83Kt)
nbnfi-fe2018121751126_solr.xml (39.77Kt)
Lataukset: 

URL:
https://doi.org/10.1109/TGRS.2018.2847026

Norberg, Johannes
Vierinen, Juha
Roininen, Lassi
Orispää, Mikko
Kauristie, Kirsti
Rideout, William C.
Coster, Anthea J.
Lehtinen, Markku S.
Institute of Electrical and Electronics Engineers
22.08.2018

J. Norberg et al., "Gaussian Markov Random Field Priors in Ionospheric 3-D Multi-Instrument Tomography," in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 12, pp. 7009-7021, Dec. 2018. doi: 10.1109/TGRS.2018.2847026

https://rightsstatements.org/vocab/InC/1.0/
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/TGRS.2018.2847026
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2018121751126
Tiivistelmä

Abstract

In 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.

Kokoelmat
  • Avoin saatavuus [37688]
oulurepo@oulu.fiOulun yliopiston kirjastoOuluCRISLaturiMuuntaja
SaavutettavuusselosteTietosuojailmoitusYlläpidon kirjautuminen
 

Selaa kokoelmaa

NimekkeetTekijätJulkaisuajatAsiasanatUusimmatSivukartta

Omat tiedot

Kirjaudu sisäänRekisteröidy
oulurepo@oulu.fiOulun yliopiston kirjastoOuluCRISLaturiMuuntaja
SaavutettavuusselosteTietosuojailmoitusYlläpidon kirjautuminen