Abstract: Compressed Sensing (CS) theory is a great breakthrough of traditional Nyquist sampling theory, it accomplishes compressive sampling and recovery of signal based on the sparsity of interested signal, the randomicity of measurement matrix and nonlinearized optimization method. Thus, as a fire-new signal processing theory, CS provides great possibilities for overcoming inherent limitations of traditional radar, and has potential to resolve many problems associated with high resolution radar, such as high sampling rate, too many dada and difficulties of real time processing. This paper first gives a brief introduction of CS principle, then radar imaging technique based on CS is discussed and analyzed in detail, thereafter a particular and comprehensive review of CS applications in high resolution radar is presented, including CS in SAR/ISAR, Through-the-Wall Radar, MIMO radar, Ground-Penetrating Radar etc., from which we can see the large potential of CS in simplifying radar hardware, conquering data limitations, improving radar imaging performance etc.. Moreover, the subsistent problems in current research and need further research are pointed out, also the advantages and disadvantages of radar imaging based on CS are summed up finally.