Various radio tomographic imaging (RTI) models and reconstruction methods are equipped with capabilities to mitigate the effects of multipath interference. This thesis combined the network shadowing (NeSh) and weighting-g models in conjunction with Tikhonov regularization and low-rank and sparse decomposition (LRSD). MATLAB was used to implement the four combinations for six experimental data sets and produce attenuation images. The attenuation images were analyzed qualitatively and quantitatively to accomplish the goal of determining which combination performed best at locating human targets. After analyzing the results, it was determined that no single combination outperformed the others for at least three out of the five quantitative metrics. Therefore, a rating technique was used instead to normalize the average results of each metric and nd the mean across each combinations newly normalized average results. In accordance with the normalization scale, the lowest and best rating revealed the optimum combination was the weighting-g model implemented in conjunction with LRSD.