Radio Frequency (RF) Tomography is a mathematical process of 3D image reconstruction from a measurement using a multistatic distribution of transmitters and receivers. The geometric diversity of these elements increases the information in the measurements. The process of determining the permittivity and conductivity profile in the measurement domain, and, therefore, the shape of the target, from the scattered field measurements, is an inverse problem. To solve this problem, under conventional methods such as the Born approximation, we use the principles of linear scattering to determine a linear relationship between measured returns and target shape. The Born approximation is valid if the scatterer is small and does not interact strongly with other objects. However, strong scatterers within the domain may generate sidelobes masking weaker returns. This masking, in conjunction with multipath effects, may result in loss of features and subsequent failure to identify a target. In this research, a novel method is proposed to increase overall image quality and extend the capabilities of RF tomography by modeling the strong scatterers in the measurement domain as dipoles that behave as secondary sources (transmitters). Unlike conventional methods, the dipole model reduces the effects of the sidelobes from the strong scatterers ix