Leveraging joint sparsity in 3D synthetic aperture radar imaging

Three-dimensional (3D) synthetic aperture radar (SAR) imaging is an active and growing field of research with various applications in both military and civilian domains. Sparsity promoting computational inverse methods have proven to be effective in providing point estimates for the volumetric image. Such techniques have been enhanced by leveraging sequential joint sparsity information from nearby aperture windows. This investigation extends these ideas by introducing a Bayesian volumetric approach that leverages the assumption of sequential joint sparsity. In addition to obtaining a point estimate, our new approach also enables uncertainty quantification. As demonstrated in simulated experiments, our approach compares favorably to currently used methodology for point estimate approximations, and has the additional advantage of providing uncertainty quantification for two-dimensional projections of the volumetric image.