Dreamer: Dual-RIS-Aided Imager in Complementary Modes

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Abstract
Reconfigurable intelligent surfaces (RISs) have emerged as a promising auxiliary technology for radio frequency imaging. However, existing works face challenges of faint and intricate back-scattered 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 the 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 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.
Funding Information
  • National Natural Science Foundation of China (12141107)
  • Guangxi Key Research and Development Program (AB21196034)
  • National Natural Science Foundation of China (62406119)
  • Natural Science Foundation of Hubei Province (2024AFB074)
  • Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence (2023B1212010001)

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