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Abstract : Computerized ionospheric tomography algorithms traditionally ignore the time variations of ionospheric distributions. Movement of ionospheric features thus becomes a source of degradation in the reconstructed images. This paper presents a new algorithm for imaging time varying ionospheric features. Since additional data sources are critical for accurate time varying reconstructions which are not overly dependent upon a priori assumptions and models, this paper also analyzes the potential contributions of GPS, DMSP and ionosonde data for time-varying image reconstruction.
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Abstract : Computerized ionospheric tomography algorithms traditionally ignore the time variations of ionospheric distributions. Movement of ionospheric features thus becomes a source of degradation in the reconstructed images. This paper presents a new algorithm for imaging time varying ionospheric features. Since additional data sources are critical for accurate time varying reconstructions which are not overly dependent upon a priori assumptions and models, this paper also analyzes the potential contributions of GPS, DMSP and ionosonde data for time-varying image reconstruction.
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