Millimeter-Wave SAR Imaging of Sparse Trajectory via Untrained Complex-Valued Neural Network

Millimeter-wave Synthetic Aperture Radar (SAR) imaging is widely used in security screening, industrial nondestructive testing, and autonomous navigation due to its high resolution and penetration capabilities. However, high-fidelity reconstruction typically requires dense spatial sampling, resulting in long scanning times and significant data acquisition overhead. Although compressed sensing has been explored for sparse reconstruction, its reliance on random sampling is incompatible with the deterministic scanning trajectories of practical SAR systems and thus fails to reduce real acquisition time. To address this, we propose mmUSAR, an unsupervised SAR imaging framework incorporating two hardware-compliant sparse scanning strategies: Expanded Vertical-interval Sampling (EVS) and Reduced Height Sampling (RHS), which reduce the number of vertical scanning positions. Reconstruction is performed by an untrained complexvalued neural network that embeds physical priors via an observation approximation operator and enforces structured sparsity regularization. The framework requires no external training data and adapts to each scene through test-time optimization. Experiments on simulated and real-world datasets show that mmUSAR achieves high-fidelity image reconstruction while reducing the scanning trajectory, thereby decreasing acquisition time by up to 75%. Compared to conventional and other untrained neural network methods, mmUSAR offers superior image quality and competitive runtime, providing a practical solution for efficient and resource constrained SAR imaging.