Linear Methods

As a countermeasure to non-linearity, linear (also known as linearized) methods seem to allow a very easy implementation and require a limited amount of computer memory and computational time. However, they suffer from several limitations induced by the adopted approximated model. Some examples of linear methods include Born and Kirchhoff approximations. In this framework, in [2]-[6] a new data-driven linear approximation is introduced based on Virtual Experiments (see the pertained section for more details), which has an extended validity range as compared to the traditional Born approximation. The same approximation is also extended in [7] to the case of non-homogenous scenario. Finally, in [1], an innovative linear inversion approach is introduced which takes advantage from the Contrast Source-Extended Born model (see details about new model based quantitative inversion).

1. R. Autieri, M. D’Urso, T. Isernia and V. Pascazio, “Inverse Profiling via an Effective Linearized Scattering Model and MRF Regularization,” IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 6, pp. 1021-1025, 2011. [click here]
2. L. Crocco, I. Catapano, L. Di Donato and T. Isernia, “The Linear Sampling Method as a Way to Quantitative Inverse Scattering,” IEEE Transactions on Antennas and Propagation, vol. 60, no. 4, pp. 1844-1853, 2012. [click here]
3. L. Di Donato, R. Palmeri, G. Sorbello, T. Isernia, and L. Crocco, “Assessing the capabilities of a new linear inversion method for quantitative microwave imaging,” International Journal of Antennas and Propagation, 2015. [click here] 
4. M. T. Bevacqua, L. Crocco, L. Di Donato, and T. Isernia, “Microwave Imaging of Non-Weak Targets via Compressive Sensing and Virtual Experiments,” IEEE Antennas and Wireless Propagation Letters, vol.14, pp.1035-1038, 2015. [click here]
5. M. T. Bevacqua and L. Di Donato, “Improved TV-CS Approches for Inverse Scattering Problem,” The Scientific World Journal, Hindawi, 2015. [click here]
6. L. Crocco, L. Di Donato, I. Catapano, and T. Isernia, “The Factorization Method for Virtual Experiments Based Quantitative Inverse Scattering,” Progress In Electromagnetics Research, vol. 157, pp. 121-131, 2016. [click here]
7. L. Di Donato, R. Palmeri, G. Sorbello, T. Isernia and L. Crocco, “A New Linear Distorted-Wave Inversion Method for Microwave Imaging via Virtual Experiments,” IEEE Transactions on Microwave Theory and Techniques, vol. 64, no. 8, pp. 2478-2488, 2016. [click here]