Super-Resolution Imaging for Ultrawideband Radar via Generalized Atomic Norm Minimization with GTD Modality Demixing

In this article, we propose a novel super-resolution method for ultrawideband radar imaging, to address the problem of degraded range estimation accuracy of off-grid targets. We propose generalized atomic norm minimization (ANM) with modality demixing, dubbed ANM-MD, which effectively harnesses the sparsity of radar targets over a continuous range space. First, we demix the radar echo of targets according to their frequency dependency modalities (FDMs) in the geometrical theory of diffraction (GTD) model. By modality demixing, we can suppress the influence of multiple FDMs on consequent estimation of target ranges. Then, we estimate the scattering parameters of radar targets separately in each FDM, leading to accurate estimation of target ranges. Experimental results show that our method can improve the accuracy of range estimation of off-grid targets by more than 15% compared with existing methods, leading to improved quality of super-resolution imaging.