1-bit SAR Imaging Based on Sparse Priors and Matrix Completion: Method and Experimental Validation

Synthetic aperture radar (SAR) is a key all-weather, day-and-night remote sensing technology. However, the growing volume of SAR data from increasing satellite deployments challenges acquisition, storage, and transmission. 1-bit quantization can mitigate these issues, yet 1-bit imaging algorithms often yield high computational complexity and residual false targets. Even advanced methods like binary iterative hard thresholding (BIHT), despite achieving acceptable quality, remain constrained by these very bottlenecks, which critically limit their utility in large ocean scenes. To address these challenges, this article proposes a 1-bit SAR imaging method based on sparse prior and matrix completion, designed for ocean scenes where targets such as ships present as sparsely distributed isolated scatterers. By exploiting scene sparsity, we construct a 2-D matrix observation model and reformulate imaging as a sparse optimization problem solved via iterative matrix completion. Compared to the traditional compressed sensing (CS)-based 1-bit imaging method, the proposed approach effectively reduces the dimensionality of the observation matrix, leading to a significant reduction in computation time. Experimental results demonstrate that the proposed method successfully suppresses false targets in simulated and measured data, achieving imaging quality close to the full precision imaging results with indicators such as peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR). This work provides a practical solution for SAR applications in sparse scenes, such as large-scale maritime ship monitoring.