Virtual Multiview Fusion for mmWave Imaging Assisted by Multiple Metasurfaces

MmWave imaging assisted by metasurfaces is a burgeoning technique attributed to its fine-grained imaging ability by time-varying phase coding, which produces a large virtual aperture. To obtain stereoscopic 3D perception, multiple metasurfaces can be utilized for virtual multiview fusion, allowing for the capture of features that are not visible from a single viewpoint. However, the multipath imaging fusion faces huge data burden and the environment clutter especially reflection from the direct path will cause disturbance. To address these issues, this paper introduces Bayesian compressive sensing to focus on the region of interest (ROI) and design a double sparse prior for high-resolution multiview image reconstruction. First, multiple metasurfaces are utilized to generate virtual multiview imaging results. Then, the Bayesian inference method is leveraged to resist environmental noise and achieve autofocusing imaging with undersampled data. The expectation propagation (EP) is introduced to estimate the statistical parameter iteratively. Further, a double sparse prior is designed based on spike-and-slab to promote inter-sparsity and intra-sparsity simultaneously. Simulation results show that our proposed system demonstrates superior performance by fusing sparse images from multiple metasurfaces arrangements.