This paper summarizes the results of an ARPA/Army sponsored program to develop innovative approaches for reducing the effects of multiple source of radio frequency interference (RFI) on synthetic aperture radars (SARs) operating in the frequency range of 100 MHz to 1000 MHz. Since the SAR signal can be modeled as wide band noise, the approach taken to achieve the objective was to model the RFI as a collection of tones within the desired SAR signal bandwidth. RFI suppression consisted of detecting the presence of and estimating the number of these tones, estimating their amplitude, phase, and frequency, and finally reconstructing the RFI and coherently subracting it from the original corrupted signal. Central to our approach was the use of a parametric maximum likelihood (PML) algorithm for the estimation of the parameters of the RFI tones. Although most of our effort was devoted to the evaluation of the performance obtainable from the PML algorithm, a variation of band-stop filtering, which is referred to as the Notch or Adaptive Mask algorithm, was also studied. Since the focus of this program was the development of algorithms for the ultra-wideband (UWB) P-3 SAR, which is a deramp SAR, a means of applying the PML algorithm to deramped RFI was also necessary. This paper will thus briefly describe the PML algorithm and how it can be applied to a deramp SAR, and it will then discuss the preformance of both RFI suppression algorithms and their computational complexity. As a result of this one year effort, two RFI algorithms have been developed that automatically remove 90 to 95 percent of the RFI that could have corrupted a SAR image.