Computer Ionospheric Tomography (CIT) is an important tool for imaging, studying and giving an overall view of the ionospheric behaviour.
It was formerly proposed by Austen et al. , where a satellite in polar-orbit was used to collect TEC observations from a chain of ground receivers. Limitations such as limited angle observations and uneven/sparse distribution of the ground stations [2, 3] make the solution unstable and difficult to solve.
The problem is said to be ill-conditioned and regularization techniques became important to stabilize it. Basis functions are also used to describe the horizontal variation of the electron density. In this processor two different regularizations and horizontal basis functions are compared :
- Tikhonov regularization. The method seeks to minimize the energy of the coefficients in some sense. Spherical harmonic basis functions are used in this case.
- Sparse regularization. The method seeks to minimize the number of basis functions needed to represent the structures in the ionosphere fittingly. Wavelet basis functions are used in this case. It is also referred as multi-resolution analysis due to the ability of wavelets to represent the structures at different scale.
Both regularizations can guarantee a unique solution under certain conditions .
Results are compared with an independent instrument (IS radar) showing the improvements obtained with sparse regularization on imaging the ionosphere at high resolution. More information can be found in .
This processor introduces also the usage of A New Ionospheric Model (ANIMo) which has been developed at the University of Bath (UK) under the TRANSMIT consortium. ANIMo is a physics-based ionospheric model and has been specifically created to support CIT techniques. The model can be implemented in various ways. ANIMo can be used to generate electron density profiles to improve the ionospheric vertical imaging. Through the application of a Data assimilation scheme, where ANIMo is used as background and GPS measurements as observation, it is possible to extend the CIT reconstructions in areas where the data coverage is very low or absent (e.g. in the middle of the ocean). The usage of this type of ingestion approaches is also expected to improve the accuracy of the final image and to perform short-term forecasting.
 Austen, J. R., Franke, S. J., Liu, C. H., Yeh, K. C.. Application of Computerized Tomography Techniques to Ionospheric Research, in:Proceedings of the Beacon Satellite Symposium 1986. Finland. University of Oulu. 25-35.
 Yeh, K.C., Raymund, T.D.. Limitations of ionospheric imaging by tomography. Radio Science, 1991. 26(6): pp. 1361-1380.
 Na, H., Lee, H.. Analysis of fundamental resolution limit of ionospheric tomography, in:Acoustics, Speech, and Signal Processing. 23-26 Mar. San Francisco, CA. IEEE. 1992. 97-100.
 T. Panicciari, N.D. Smith, F. Da Dalt, C.N. Mitchell and G.S. Bust (2014). Multiresolution Tomography of Ionospheric Electron Density, Mitigation of Ionospheric Threats to GNSS: an Appraisal of the Scientific and Technological Outputs of the TRANSMIT Project, Dr. Riccardo Notarpietro (Ed.), InTech.
 Mallat, S.. A wavelet tour of signal processing: the sparse way. 3rd ed. Burlington, MA: Academic press. 2008. 832 p.
 F. Da Dalt, C. Benton, T. Panicciari, N. D. Smith and C. N. Mitchell (2014). ANIMo — A New Ionospheric Model. Ionospheric Modeling for Ionospheric Imaging and Forecasting Purposes, Mitigation of Ionospheric Threats to GNSS: an Appraisal of the Scientific and Technological Outputs of the TRANSMIT Project, Dr. Riccardo Notarpietro (Ed.), InTech.