Matrix-Based Approach for Metasurface Radar Imaging Leveraging Multiple Reflections in Reverberating Environments

This research explores the development and implementation of a radar imaging system for indoor applications. The proposed system comprises two distinct arms, an emitter and a receiver, leveraging metasurface technology (Reconfigurable Intelligent Surface, RIS) developed by GreenerWave. Operating within the millimeter-wave range (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$60-77 \text{GHz}$</tex>), this system is envisioned for diverse applications, including automotive and healthcare industries, where the challenge of imaging within reverberating environments is paramount. The primary focus of this work is to enhance radar resolution by exploiting the multiple reflections inherent in confined cavity environments. This is achieved through a matrix-based approach aimed at accurately estimating the Green's function inside the cavity. Initially, an analytical iterative method is employed to account for all possible wall reflections, providing a comprehensive model of the cavity's Green's function. Subsequently, a faster modeling technique, based on image theory, is implemented, yielding an accurate and computationally efficient estimation of the Green's function. However, this approach requires prior knowledge of the environment. A more robust method is then developed for learning the Green's function without knowing the environment. Based on the reflection matrix concept, it exploits angular correlations of the wave field in order to estimate the Green's function linking each of the metasurface elements and reflectors in the environment. This Green's function is then exploited to harness multiple reflected waves and leverage them for high-resolution imaging in a cavity. While the resolution of conventional imaging methods is limited by the metasurface aperture, the proposed approach provides an optimal resolution close to the diffraction limit. This work not only advances the field of radar imaging but also opens new avenues for high-resolution imaging in challenging environments where conventional methods fall short.