Blind Focusing for Computational Microwave Imaging With Metasurface Aperture Based on Sparse Bayesian Learning

Computational microwave imaging with metasurface aperture (MA-CMI) is an emerging real aperture imaging scheme. It is widely concerned because of its advantages of low hardware complexity, low manufacturing cost, and high sampling rate. However, the existing studies require the MA-CMI system to remain relatively stationary with the region of interest (ROI), which greatly limits its application. Again, if there is a relative motion between the MA-CMI system and the ROI, the correlation between the measured signal and the sensing matrix of the MA-CMI system will be lost, resulting in the defocus of the imaging results and the degradation of the imaging quality. In this article, our work focuses on the problem that the imaging results are out of focus due to the unknown lateral motion between the MA-CMI system and the ROI, that is, the blind focus imaging of moving scenes based on the MA-CMI system. First, according to the measurement mechanism of the MA-CMI system, an observation model that considers the lateral motion of the ROI is established. Subsequently, based on the model, we develop a robust alternate iterative method called Newton-sparse Bayesian learning (Nt-SBL). Specifically, in each iteration, the Newton method was used to estimate the unknown velocity parameters of the motion scene; on the other hand, based on the current velocity estimation, the scene reconstruction was performed using the generic SBL. Finally, based on the MA-CMI system prototype we designed, both simulation and measured experiments are performed to verify the effectiveness of the proposed method.