Compressed sensing in ISAR imaging with sparse sub-aperture

In this paper, a reconstruction method of 2-D ISAR image with sparse sub-aperture based on Compressive Sensing (CS) is proposed. The method is revealed as follows. In the range, the conventional processing can be carried out to obtain sparse spectrogram. In the cross-range, the CS theory can be utilized to reconstruct 2-D ISAR image. According to CS theory, a random partial unit matrix can be considered as the measurement matrix and the DFT matrix can be designed as the sparsity matrix, whose multiplication satisfies RIP. In addition, the modified OMP algorithm can be utilized to achieve the reconstructed results avoiding sparsity estimation. By using the proposed method, the well 2-D ISAR imaging can be obtained on condition of the data in cross-range is reduced apparently. The effectiveness of the method can be validated in simulation results.