FOUR-DIMENSIONAL SAR IMAGING SCHEME BASED ON COMPRESSIVE SENSING

The observation data obtained from 4-D synthetic aperture radar system is sparse and non-uniform in the baseline-time plane. Hence, the imaging results acquired by traditional Fourier-based methods are limited by high sidelobes. Considering the sparse structure of actual target space in high frequency radar application, a novel 4-D imaging scheme based on compressive sensing is proposed in this paper. Firstly, the azimuth-slant range image is acquired by traditional pulse compression. Then, the basis matrix and the measurement matrix are constructed based on the sparse distribution of the radar positions and the signal form after the azimuth-slant range compression. Moreover, a weighted matrix related to the supporting field of the target is introduced to the cost function. Finally, the elevation-velocity image is reconstructed with this new cost function. Simulation results confirm the effectiveness of the proposed method.