Modified multiple measurement vectors model for squinted synthetic aperture radar imaging

Abstract

Inspired by the theoretical advances of compressed sensing, lots of sparsity-aware methods have been proposed for squinted synthetic aperture radar (SAR) imaging based on the single-measurement vector (SMV) model. Compared with SMV, the multiple measurement vectors (MMV) model has been demonstrated to have better reconstruction performance. In fact, echo received by SAR at different azimuth positions can be viewed as MMVs. However, the MMV model cannot be directly used in squinted SAR imaging, because MMV requires multiple sparse vectors of the common sparse structures, while the high-resolution range profiles (HRRPs) obtained by squinted SAR at different azimuth positions have different sparse structures due to range migration effect. A squinted SAR imaging method is proposed based on MMV. First, a modified MMV model that considers range migration is built to realize sparse representation of echo. Additionally, an improved orthogonal matching pursuit algorithm is developed to reconstruct HRRPs. Finally, a high-resolution two-dimensional image result can be easily achieved via traditional azimuth match filtering. Experimental results based on both simulated and real data demonstrate that the proposed MMV-based method can provide better computational efficiency and antinoise ability compared to the SMV-based method.