In view of the disadvantages of sampling at a high rate and long time duration in the current methods of multiple targets imaging, a novel algorithm of chirp rate estimating and Inverse Synthetic Aperture Radar (ISAR) imaging of Multiple Targets is proposed, combined with Compressive Sensing theory based on sparse sampling. Firstly, a redundancy sparse dictionary is established according to the characteristic of echo signals, the echo signals are decomposed into the sparse dictionary by utilizing Gauss random measurement matrices and focal undetermined system solver (FOCUSS) method to recover the imaging, and estimate the chirp rate accurately. Secondly, the echo separation and the motion compensation are done with the chirp rate estimated. Finally, obtaining the image of each target based on sparse sampling. And the results show that the method is feasible and valid.