RCRC-Guided Low-Tubal-Rank Tensor Recovery for Sparse Aperture Terahertz Video SAR Imaging

THz-SAR provides high-resolution imaging but is highly sensitive to trajectory perturbations, while video SAR further requires efficient reconstruction of consecutive sub-aperture frames from incomplete measurements. Under variable curvature motion, forward-model errors introduce geometric and phase inconsistencies that weaken the interframe correlation exploited by tensor recovery. This article develops an integrated radius-of-curvature change rate (RCRC)-guided low-tubal-rank recovery framework for sparse aperture THz video SAR. An RCRC-aware variable curvature trajectory operator models deterministic motion-dependent effects, a tensor nuclear-norm prior exploits interframe redundancy, and pulsewise unit modulus phase factors compensate for residual perturbations. The resulting joint problem is solved by alternating proximal-gradient, tensor singular value thresholding, and closed-form phase updates. Controlled point target simulations and measured data experiments are conducted at pulse retention ratios of 60%, 40%, and 30%. A single global slow-time mask is applied to the shared pulse stream before sub-aperture slicing, ensuring consistent pulse omissions across overlapping frames. Comparisons with three literature baselines evaluate the integrated method, while two internal ablations examine the effects of RCRC modeling and residual phase estimation within the adopted TNN-regularized formulation. The 40% case preserves the principal scattering structure and is treated as a candidate sparse observation operating point, whereas the degradation at 30% identifies a more severe robustness boundary. Because the sparse observations are generated retrospectively from full-rate measurements, the experiments validate reconstruction under missing pulses but do not demonstrate physical reductions in PRF, transmitted pulses, or collected raw data volume.