A LOW-COMPLEXITY ALGORITHM FOR FAST ONE-BIT SAR IMAGING

Imaging based on direct matched filtering of one-bit quantized echo signals results in significant degradation of imaging quality, deterioration of the Signal-to-Noise Ratio (SNR), and the generation of false targets. One-bit compressed sensing technology enables the recovery of one-bit quantized signals, and under the prior assumption of sparsity in the scene, the one-bit compressed sensing recovery algorithm can reconstruct the scattering characteristics of the scene while effectively suppressing the generation of false targets. However, existing Synthetic Aperture Radar (SAR) imaging algorithms based on one-bit compressed sensing typically employ vector observation models, which leads to high-dimensional matrices and limit their applicability to large-scale sparse imaging scenarios. In this paper, we propose a low-complexity fast one-bit SAR imaging algorithm, which uses a matrix observation model to decompose the vector observation model in two dimensions. Finally, simulation validation was carried out and the experimental results based on point and surface targets verified the effectiveness of the algorithm.