A Tanh-Based Weighted Iterative Hard Thresholding Algorithm for One-Bit SAR Imaging

One-bit synthetic aperture radar (SAR) imaging offers significant advantages in reducing the hardware burden of the system. However, one-bit measurements may introduce sign flips due to noise interference, resulting in artifacts in the imaging results. Considering that targets usually exhibit local clustering distributions in real SAR imaging scenes, in this paper, we propose a tanh-based weighted iterative hard thresholding (TW-IHT) algorithm that associates each element in the scene with its neighboring elements, utilizing weighted and hard thresholding operations to suppress pseudo-targets caused by noise. Moreover, we replace the sign function with the tanh function to facilitate gradient computation. Both simulation and measured data processing results demonstrate that the proposed algorithm has superior one-bit SAR imaging results in dealing with continuous targets compared to other algorithms.