Compressed sensing SAR imaging based on sparse representation in fractional Fourier domain

Compressed sensing (CS) is a new technique of utilizing a priori knowledge on sparsity of data in a certain domain for minimizing necessary number of measurements. Based on this idea, this paper proposes a novel synthetic aperture radar (SAR) imaging approach by exploiting sparseness of echo data in the fractional Fourier domain. The effectiveness and robustness of the approach are assessed by some numerical experiments under various noisy conditions and different measurement matrices. Experimental results have shown that, the obtained images by using the CS technique depend on measurement matrix and have higher output signal to noise ratio than traditional pulse compression technique. Finally simulated and real data are also processed and the achieved results show that the proposed approach is capable of reconstructing the image of targets and effectively suppressing noise.