摘要 : A new formalism for scalar tomographic inversion is developed for two-dimensional radio frequency tomography. A least squares minimization of the difference between predicted and measured data is performed in order to obtain the l... 展开 A new formalism for scalar tomographic inversion is developed for two-dimensional radio frequency tomography. A least squares minimization of the difference between predicted and measured data is performed in order to obtain the lossless permittivity distribution. The novelty of the method is twofold. First, a multifrequency near-field two-dimensional scattering forward problem is solved via Richmond's moment method. Second, the tomographic inversion is regularized using a roughness constraint on the model permittivity. 收起