Sparsity-based three-dimensional image reconstruction for near-field MIMO radar imaging

Abstract

Near-field multiple-input multiple-output (MIMO) radar imaging systems are of interest in diverse fieldssuch as medicine, through-wall imaging, airport security, concealed weapon detection, and surveillance. The successfuloperation of these radar imaging systems highly depends on the quality of the images reconstructed from radar data.Since the underlying scenes can be typically represented sparsely in some transform domain, sparsity priors can effectivelyregularize the image formation problem and hence enable high-quality reconstructions. In this paper, we developan efficient three-dimensional image reconstruction method that exploits sparsity in near-field MIMO radar imaging.Sparsity is enforced using total variation regularization, and the reflectivity distribution is reconstructed iterativelywithout requiring computation with huge matrices. The performance of the developed algorithm is illustrated throughnumerical simulations. The results demonstrate the effectiveness of the sparsity-based method compared to a classicalimage reconstruction method in terms of image quality.