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Dreamer: Dual-RIS-Aided Imager in Complementary Modes
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AI
DOI：10.1109/TAP.2025.3553767.png)
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Abstract
En 中文
Reconfigurable intelligent surfaces (RISs) have emerged as a promising auxiliary technology for radio frequency imaging. However, existing works face challenges of faint and intricate backscattered waves and the restricted field of view (FoV), both resulting from complex target structures and a limited number of antennas. The synergistic benefits of multi-RIS-aided imaging hold promise for addressing these challenges. Here, we propose a dual-RIS-aided imaging system, Dreamer, which operates collaboratively in complementary modes (reflection mode and transmission mode). Dreamer significantly expands FoV and enhances perception by deploying dual-RIS across various spatial and measurement patterns. Specifically, we perform a fine-grained analysis of how radio-frequency (RF) signals encode scene information in the scattered object modeling. Based on this modeling, we design illumination strategies to balance spatial resolution and observation scale and implement a prototype system in a typical indoor environment. Moreover, we design a novel artificial neural network with a CNN-external-attention mechanism to translate RF signals into high-resolution images of human silhouettes. Our approach achieves an impressive structural similarity index (SSIM) score of 0.83 surpassing state-of-the-art solutions, validating its effectiveness in broadening perception modes and enhancing imaging capabilities. The code to reproduce our results is available at: https://github.com/fuhaiwang/Dreamer.
Keywords:
Computational electromagnetic (EM) imaging
deep learning of inverse scattering problems (ISPs)
indoor imaging
reconfigurable intelligent surface (RIS)
transmission-reflection mode
Journal
IF:
5.8
Papers:
502
Citations:
6.8W