PRF-Reduced Sliding Spotlight SAR Imaging With Joint Sparse Representation Model

With the expansion of the surveillance area and resolution of spaceborne synthetic aperture radar (SAR) systems, the increasing amount of echo data requires further research on efficient imaging methods. The pulse repetition frequency (PRF) of traditional SAR needs to satisfy the Shannon–Nyquist sampling theory, while the PRF limits the system swath width, making it impossible to achieve wider illumination coverage. The reduction of the PRF can effectively increase the swath width, but it will cause severe azimuth ambiguity, decreasing the image quality. Several methods have been introduced for ambiguity suppression, but many of them become ineffective at lower PRFs. In this article, we propose a novel sparse imaging method for the spaceborne PRF-reduced sliding spotlight SAR. The proposed method considers the azimuth ambiguity term in the joint sparse imaging model, separately constrains the main imaging and azimuth ambiguity areas, and achieves azimuth ambiguity suppression by using compressive sensing (CS) technology. With its help, we can reduce the original PRF by up to half to double the swath width and enable high-precision sparse reconstruction of large-scale scenes. Compared with $L_{2,1}$ -norm regularization-based algorithm, the proposed method shows the superior ambiguity suppression ability with less computational cost from PRF-reduced echo data. Experimental results on simulated raw data validate the proposed method.