Application of stochastic inverse theory to ionospheric tomography

Fremouw, E. J.; Secan, J. A.; Howe, Bruce M.

doi:10.1029/92rs00515

Cited by 156 publications

(101 citation statements)

References 11 publications

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“…Since Austen et al, [4] applied the algebraic reconstruction technique (ART) to the twodimensional analysis of model TEC data generated by a computer simulation various methods for CIT were proposed and applied to analyses of model data and real observation data. Though the two-dimensional ionospheric tomography has been studied extensively from both the theoretical and observational aspects [5][6][7], in these studies only the ionospheric structure within a cross-section defined by the satellite orbit and the ground receiver array can be obtained, which limits strongly the observation time and place. In order to cope with the problem methods for author's e-mail: takeda-t@gakushikai.jp the three-dimensional CIT are being studied intensively by many authors [8][9][10][11][12][13][14].…”

Section: Computerized Ionospheric Tomographymentioning

confidence: 99%

Ionospheric Tomography by Neural Network Collocation Method

Takeda

2007

Plasma and Fusion Research

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We describe a neural network collocation method (NNCM) for tomographic image reconstruction with small amount of projection data, which has been successfully applied to the three-dimensional ionospheric tomography based on the dataset of signal delays from the GPS satellites. In NNCM the neural network is trained by minimizing an object function composed of squared residuals of the governing equations evaluated at the collocation points and some constraining conditions imposed usually by observation data. This method is applied not only to the computerized tomography but also to the analyses of various inverse problems such as the data assimilation, the parameter estimation, the time series prediction, and so on.

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Section: Computerized Ionospheric Tomographymentioning

confidence: 99%

Ionospheric Tomography by Neural Network Collocation Method

Takeda

2007

Plasma and Fusion Research

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“…The tomographic images used in the present study were reconstructed by the DIT/ART method ] and created in a pixelized grid of some 0.25°in latitude and 15 km in altitude. The Discrete Inversion Theory (DIT) procedure described by Fremouw et al [1992] was used to reconstruct the large-scale background features in the ionosphere, while subsequent processing by the Algebraic Reconstruction Technique (ART) formed the smaller-scale density structures. Ionosonde measurements at Chilton (51.5°N; 1.3°W) made within 15 minutes of a satellite pass have been incorporated into the tomographic inversion to improve the reconstruction of the vertical profile of the bottom-side ionosphere.…”

Section: Radio Tomographic Imagingmentioning

confidence: 99%

Radio tomographic imaging as an aid to modeling of ionospheric electron density

Dabas

Kersley

2003

Radio Science

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[1] Models of the ionosphere, used in applications for the prediction or correction of propagation effects on practical radio systems, are often inadequate in their representation of the structure and development of large-scale features in the electron density. Over northern Europe, characterization of the main trough presents particular problems for such empirical or parameterized models and hence for radio propagation forecasting and ionospheric mapping. Results are presented from a study aimed at investigating the possible role of radio tomographic imaging in adapting models to yield a better representation of the ionosphere over Europe. It is shown that use of radio tomography gives better agreement with actual ionosonde data than can be obtained from any of the models used alone. It is suggested that the technique may have a possible role in the mapping of ionospheric conditions in near-real time for future systems applications. Citation: Dabas, R. S., and L. Kersley, Radio tomographic imaging as an aid to modeling of ionospheric electron density,

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“…A different method of ionospheric tomography is provided by stochastic inversion, which has been applied by Fremouw et al [1992], Markkanen et al…”

Section: Introductionmentioning

confidence: 99%

Stochastic inversion in ionospheric radiotomography

Nygrén¹,

Markkanen²,

Lehtinen³

et al. 1997

Radio Science

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Abstract. The stochastic inversion method in ionospheric radiotomography is reviewed with a special emphasis on regularization used in the inversion process. Regularization is used both for preventing vigorous point-to-point oscillations and for controlling the peak altitude and thickness of the inversion result. The latter usually means importing a priori information on the layer height and thickness to the solver. In this paper it is pointed out some information on the ionospheric altitude and the profile shape even in the case of a purely horizontally stratified layer. If this information could be used in choosing an appropriate regularization, no additional information would be needed. Simulation tests are presented which indicate that the altitude of a horizontally stratified layer can be determined with a reasonable accuracy without any a priori information. An attempt is also made to use the data for determining the shape of a proper regularization profile. Although some success is achieved in this effort, it is concluded that available a priori information, for example, ionosonde or incoherent scatter measurements, should be used in choosing the regularization profile. The ideas are tested with true data obtained from difference Doppler measurements carried out in Scandinavia, and the results are compared with simultaneous observations made by the European incoherent scatter radar. The comparison shows a reasonable agreement, although clear discrepancies also occur, for instance, in the shape of the bottomside profile.

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Application of stochastic inverse theory to ionospheric tomography

Cited by 156 publications

References 11 publications

Ionospheric Tomography by Neural Network Collocation Method

Ionospheric Tomography by Neural Network Collocation Method

Radio tomographic imaging as an aid to modeling of ionospheric electron density

Stochastic inversion in ionospheric radiotomography

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