Robust Ground Moving Target Imaging Using Defocused RoI Data and Sparsity-Based ADMM Autofocus Under Terahertz Video SAR

Ground moving target imaging (GMTIm) presents both challenges and opportunities for enhancing the fine-grained situational awareness and target recognition. However, the imaging quality of non-cooperative moving targets (MTs) fundamentally depends on their motion characteristics, which often fluctuate unpredictably during long dwell times in microwave bands. To address these limitations, this paper proposes a robust GMTIm scheme based on terahertz video synthetic aperture radar (THz-ViSAR). Rather than operating on complete echo data directly, the proposed method achieves GMTIm through the defocused region of interest (RoI) data inversion, which can reduce the computation burden. Benefiting from the short dwell time of THz-ViSAR, motion patterns of MTs can be simplified as the uniform motion in most cases. In response to range walk (RW) manifested by radial motion of MTs, the RW correction is employed based on the inclination of range profiles. Thus, considering the sparsity of MTs, motion errors caused by azimuth velocities from MTs, can be compensated by the minimum image entropy-based alternating direction method of multipliers (ADMM) autofocus framework, which can deal with the unknown phase errors and improve the imaging quality adaptively. Finally, the azimuth scaling is established by the azimuth spreading coefficient table, which is constructed based on the target velocity, rotating radius and center angle. Simulations and airborne field experiments are carried out to validate the effectiveness of the proposed method.