A 1-Bit Compressive Sensing Approach for SAR Imaging Based on Approximated Observation

Compressive sensing (CS) theory has achieved significant success in the field of synthetic aperture radar (SAR) imaging. Recent studies have shown that SAR imaging for sparse scene can also be successfully performed with 1-bit quantized data. Existing reconstruction algorithms always involve large matrix-vector multiplications which make them much more time and memory consuming than traditional matched filtering (MF) -based focusing methods because the latter can be effectively implemented by FFT. In this paper, a novel CS approach named BCS-AO for SAR imaging with 1-bit quantized data is proposed. It adopts the approximated SAR observation model deduced from the inverse of MF-based methods and is solved by an iterative thresholding algorithm. The BCS-AO can handle large-scaled data because it uses MF-based fast solver and its inverse to approximate the large matrix-vector multiplications. Both the simulated and real data are processed to test the performance of the novel algorithm. The results demonstrate that BCS-AO can perform sparse SAR imaging effectively with 1-bit quantized data for large scale applications.