Radio Astronomy, the study of distant objects in the radio spectrum, is set at defiance against all other users of the electromagnetic spectrum. Where traditional use would fill the electromagnetic spectrum with as much transmission and information as possible, Radio Astronomy would rather no man-made signals existed. Since that is not possible, they grumble and have to deal with unwanted transmissions impinging upon their instruments. I have demonstrated that subspace projection can remove these unwanted signals from Radio Astronomical data in post processing. I demonstrate it both on simulated data and on data taken from the Very Large Array radio telescope.In the process of implementing the algorithm, I show that the self power terms contain necessary information about the array element responses to RFI sources. While the autocorrelation are not used in the normal synthesis imaging process, my work shows that RFI mitigation using Subspace Projection performs better with the autocorrelations retained when computing projection matrices. Furthermore, I demonstrate that proper data collection allows a significant decrease in error under subspace projection. Potential enhancements to subspace projection are also briefly considered.