Abstract: Recent theory of Compressed Sensing (CS) suggests that exact recovery of an unknown sparse signal can be achieved from few measurements with overwhelming probability. In this paper, CS technology is combined with random noise radar and the concept of random noise radar is proposed based on CS. The block diagram of the radar system is presented. Detailed analysis show that the sensing matrix of the random noise radar based on CS exerts good Restricted Isometry Property (RIP). Given a sparse or transform sparse target scene, the random nose radar based on CS can get high accuracy image by collecting far less amount of echo data than conventional noise radar does. Finally, in this paper, the conclusions are all demonstrated by simulation experiments.