Ionospheric tomography in Bayesian framework with Gaussian Markov random field priors

Norberg, L j Johannes; Roininen, Lassi; Vierinen, Juha; Amm, O.; McKay-Bukowski, D.; Lehtinen, Markku

doi:10.1002/2014rs005431

“…There are different algorithms available for ionospheric tomography, e.g., the Algebraic Reconstruction Technique or the Bayesian statistical approach . Our three‐dimensional tomography is based on a constrained least squares method with optimized constraining parameters .…”

Section: ‐D Tomographymentioning

confidence: 99%

Real-Time Ionosphere Monitoring by Three-Dimensional Tomography over Japan

Saito

¹

,

Suzuki

²

,

Yamamoto

³

et al. 2017

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This paper reports on the development of a real-time three-dimensional ionospheric tomography technique using GNSS measurements. GPS measurement data from 200 GPS receivers distributed throughout Japan are analyzed in real time to calculate ionospheric Total Electron Contents (TECs). The three-dimensional ionospheric electron density distribution was reconstructed using the tomography technique from the slant TEC data. The tomography results were found to be in good agreement with both the electron density profiles obtained by radio occultation measurements by the COSMIC satellite and the ionospheric peak densities obtained by vertical radio sounding measurements (ionosondes). The real-time ionospheric tomography system commenced operation in March 2016 and it continuously produces three-dimensional electron density distributions over Japan every 15 min with latency of about 6 min. This system is a very powerful tool for monitoring the ionosphere in real time, which could both improve the performance of GNSS-based systems and further the understanding of ionospheric variations.

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“…In this article we continue the work presented in (Norberg et al, 2015) and include the ionosonde measurements in the Bayesian statistical inversion approach for ionospheric tomography. For comparison, we analyze the data also with the prior mean taken from the International Reference Ionosphere (IRI) model, and with a zero-mean prior.…”

Section: Introductionmentioning

confidence: 90%

“…(6) only the precision matrix −1 pr (i.e., the inverse of the prior covariance) is required besides the prior mean. In (Norberg et al, 2015) it is shown how the precision matrix of a known covariance can be constructed with a sparse matrix representation with Gaussian Markov random fields. The approach provides us with the interpretation of a probability distribution, yet it keeps the approach computationally feasible, in comparison to operating with full covariance matrices.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian statistical ionospheric tomography improved by incorporating ionosonde measurements

Norberg

¹

,

Virtanen

²

,

Roininen

³

et al. 2016

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Abstract. We validate two-dimensional ionospheric tomography reconstructions against EISCAT incoherent scatter radar measurements. Our tomography method is based on Bayesian statistical inversion with prior distribution given by its mean and covariance. We employ ionosonde measurements for the choice of the prior mean and covariance parameters and use the Gaussian Markov random fields as a sparse matrix approximation for the numerical computations. This results in a computationally efficient tomographic inversion algorithm with clear probabilistic interpretation.We demonstrate how this method works with simultaneous beacon satellite and ionosonde measurements obtained in northern Scandinavia. The performance is compared with results obtained with a zero-mean prior and with the prior mean taken from the International Reference Ionosphere 2007 model. In validating the results, we use EISCAT ultrahigh-frequency incoherent scatter radar measurements as the ground truth for the ionization profile shape.We find that in comparison to the alternative prior information sources, ionosonde measurements improve the reconstruction by adding accurate information about the absolute value and the altitude distribution of electron density. With an ionosonde at continuous disposal, the presented method enhances stand-alone near-real-time ionospheric tomography for the given conditions significantly.

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“…There are different algorithms available for ionospheric tomography, e.g., the Algebraic Reconstruction Technique [2,10] or the Bayesian statistical approach [3,11]. Our three-dimensional tomography is based on a constrained least squares method with optimized constraining parameters [6,8].…”

Section: -D Tomographymentioning

confidence: 99%

Real-time Ionosphere Monitoring by Three-dimensional Tomography Over Japan

Saito

¹

,

Suzuki²,

Yamamoto³

et al. 2016

Proceedings of the 29th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 201

View full text Add to dashboard Cite

This paper reports on the development of a real-time three-dimensional ionospheric tomography technique using GNSS measurements. GPS measurement data from 200 GPS receivers distributed throughout Japan are analyzed in real time to calculate ionospheric Total Electron Contents (TECs). The three-dimensional ionospheric electron density distribution was reconstructed using the tomography technique from the slant TEC data. The tomography results were found to be in good agreement with both the electron density profiles obtained by radio occultation measurements by the COSMIC satellite and the ionospheric peak densities obtained by vertical radio sounding measurements (ionosondes). The real-time ionospheric tomography system commenced operation in March 2016 and it continuously produces three-dimensional electron density distributions over Japan every 15 min with latency of about 6 min. This system is a very powerful tool for monitoring the ionosphere in real time, which could both improve the performance of GNSS-based systems and further the understanding of ionospheric variations.

show abstract

Ionospheric tomography in Bayesian framework with Gaussian Markov random field priors

Cited by 34 publications

References 26 publications

Real-Time Ionosphere Monitoring by Three-Dimensional Tomography over Japan

Real-Time Ionosphere Monitoring by Three-Dimensional Tomography over Japan

Bayesian statistical ionospheric tomography improved by incorporating ionosonde measurements

Real-time Ionosphere Monitoring by Three-dimensional Tomography Over Japan

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