dot
Detailansicht
Katalogkarte GBA
Katalogkarte ISBD
Suche präzisieren
Drucken
Download RIS
Hier klicken, um den Treffer aus der Auswahl zu entfernen
Titel Using sparse regularization for multiresolution tomography of the ionosphere
VerfasserIn T. Panicciari, N. D. Smith, C. N. Mitchell, F. Da Dalt, P. S. J. Spencer
Medientyp Artikel
Sprache Englisch
ISSN 2198-5634
Digitales Dokument URL
Erschienen In: Nonlinear Processes in Geophysics Discussions ; 2, no. 2 ; Nr. 2, no. 2 (2015-03-25), S.537-572
Datensatznummer 250115156
Publikation (Nr.) Volltext-Dokument vorhandencopernicus.org/npgd-2-537-2015.pdf
 
Zusammenfassung
Computerized ionospheric tomography (CIT) is a technique that allows reconstructing the state of the ionosphere in terms of electron content from a set of Slant Total Electron Content (STEC) measurements. It is usually denoted as an inverse problem. In this experiment, the measurements are considered coming from the phase of the GPS signal and, therefore, affected by bias. For this reason the STEC cannot be considered in absolute terms but rather in relative terms. Measurements are collected from receivers not evenly distributed in space and together with limitations such as angle and density of the observations, they are the cause of instability in the operation of inversion. Furthermore, the ionosphere is a dynamic medium whose processes are continuously changing in time and space. This can affect CIT by limiting the accuracy in resolving structures and the processes that describe the ionosphere. Some inversion techniques are based on l2 minimization algorithms (i.e. Tikhonov regularization) and a standard approach is implemented here using spherical harmonics as a reference to compare the new method. A new approach is proposed for CIT that aims to permit sparsity in the reconstruction coefficients by using wavelet basis functions. It is based on the l1 minimization technique and wavelet basis functions due to their properties of compact representation. The l1 minimization is selected because it can optimise the result with an uneven distribution of observations by exploiting the localization property of wavelets. Also illustrated is how the interfrequency biases on the STEC are calibrated within the operation of inversion, and this is used as a way for evaluating the accuracy of the method. The technique is demonstrated using a simulation, showing the advantage of l1 minimization to estimate the coefficients over the l2 minimization. This is in particular true for an uneven observation geometry and especially for multi resolution CIT.
 
Teil von