Radar cross section (RCS) measurement technology based on three-dimensional imaging is a flexible and efficient RCS measurement technology that has emerged in recent years. The technology is based on a three-dimensional linear array synthetic aperture radar (SAR) measurement system to perform near-field three-dimensional high-resolution imaging of the target, and obtain the far-field RCS of the target through the near-far-field transformation algorithm. Both near-field and outer-field measurements can be achieved by this technology. The measurement accuracy of RCS is directly affected by the imaging accuracy of the target. Therefore, in order to obtain high-quality RCS results, it is necessary to obtain high-resolution images. In this paper, the compressed sensing three-dimensional imaging algorithm is used to image the target, which effectively improves the inversion accuracy of RCS, and the principle of sidelobe suppression of the improved CLEAN algorithm is analyzed. It is concluded that the two methods are based on the principle of deconvolution to suppress sidelobes. The simulation results of MATLAB and FEKO show that the compressed sensing algorithm can more effectively extract the scattering center and improve the inversion accuracy of RCS.