Weighted L1-Norm for One-Bit Compressed Sensing Based on Approximated Observation

In recent years, one-bit compressed sensing (CS) has been applied to the field of synthetic aperture radar (SAR) imaging and shows great potential. The existing models are, based on application of the sensing matrix acquired by exact observation functions. As a result, the corresponding reconstruction algorithms are much more time consuming than traditional matched filter (MF)-based focusing methods, especially in high resolution and wide swath systems. This paper presents a novel one-bit compressed sensing approach for SAR imaging using weighted L1-Norm Minimization based on approximated observation.It adopts the approximated SAR observation model deduced from the inverse of MF-based methods.It can be applied to high-quality and high-resolution imaging under sub-Nyquist rate sampling, while decreasing the computational cost substantially both in memory and time, resulting in a fast approach.Simulations results show that our method can perform sparse SAR imaging effectively with one-bit quantized data for large scale applications.