Adaptive One-bit Quantization for SAR Imaging

Recently, one-bit compressed sensing (CS) with time-varying thresholds has received lots of attentions. In this study, we propose a novel one-bit compressed sensing synthetic aperture radar (SAR) imaging method with adaptive quantization in which the thresholds are updated adaptively, and the Logistic Regression algorithm with $L_{1}$ norm regularization is used to recover the original reflectivity coefficient of the sparse targets in scenes. Simulation results show that the proposed method can accurately recover the SAR image with much less echo data than that required by the Nyquist rate and outperform the random quantization scheme. Moreover, the cost of hardware and energy consumption are reduced for radar systems.