Mixed sparse representation for approximated observation-based compressed sensing radar imaging

Abstract

Recently, compressed sensing (CS) has been applied in synthetic aperture radar (SAR). A framework of mixed sparse representation (MSR) has been proposed for reconstructing SAR images due to the complicated ground features. The existing method decomposes the image into the point and smooth components, where the sparse constraint is directly applied to the smooth components. This makes it difficult to tackle the complex-valued SAR images, since the phase angles of SAR images are always stochastic. A magnitude-phase separation MSR method is proposed for CS-SAR imaging based on approximated observation. Compared to the existing method, the proposed method has better reconstruction ability, because it only imposes the sparse constraint on the magnitude of the smooth components, and therefore, the phase angles are still stochastic. Furthermore, owing to the inherent low memory requirement of approximated observation, the proposed method requires much less memory cost. In the simulation and experimental results, the proposed method deals with the complex-valued SAR images effectively and demonstrates superior performance over the chirp scaling algorithm and the existing MSR method.