Dimension-reduced radio astronomical imaging based on sparse reconstruction

Modern radio telescopes commonly use antenna arrays to achieve high-resolution imaging, and various beamforming techniques have been developed in radio astronomy to generate dirty images. Because the manifold of a radio telescope array varies over time due to Earth rotation, beamformers are separately designed and implemented at each time epoch, and the resulting images are averaged over multiple epochs to form enhanced dirty images. Because astronomical scenes are typically sparse, we propose a new method through sparse reconstruction to obtain clean astronomical images. To reduce the computational complexity, a singular value decomposition based compressive sensing scheme is applied. The proposed method offers reduced computational complexity while maintaining the high quality of the sparse reconstruction. Unlike traditional beamforming techniques which require an additional deconvolution procedure for clean image formation, the proposed technique provides clean astronomical images directly with accurate estimation of the source position and intensity.