RIS-Aided Sensing: Experimental Validation of Radar 3D Imaging in the mmWave Band

TL;DR

This paper experimentally validates a mmWave 3D imaging system combining FMCW radar, RIS, and VNA, demonstrating accurate reconstruction in diverse scenarios for potential industrial and security uses.

Contribution

It introduces an autonomous RIS-aided 3D imaging framework with real-time localization and optimized RIS control, validated through multiple practical scenarios.

Findings

Accurate 3D reconstruction achieved with reduced angular resolution.

System successfully detects and reconstructs multiple targets including humans and vehicles.

Experimental validation confirms feasibility for industrial and security applications.

Abstract

The transition toward 6G networks demands energy-efficient hardware capable of active interaction with the environment. Reconfigurable Intelligent Surfaces (RIS) have emerged as a key technology for Integrated Sensing and Communications (ISAC), enabling geometric environment recognition with minimal power consumption. However, achieving targeted 3D spatial mapping in a fully autonomous, closed-loop system remains a significant challenge. In this work, we validate experimentally an autonomous mmWave 3D imaging framework that integrates an Frequency-Modulated Continuous Wave (FMCW) radar with a 1-bit RIS and a Vector Network Analyzer (VNA) to perform targeted 3D reconstruction. The FMCW radar acts as a coarse localizer, providing real-time spatial priors to define dynamic Regions of Interest (ROI). These coordinates are translated into optimized RIS phase profiles to perform Stepped-Frequency Continuous-Wave (SFCW) measurements. We experimentally validate the system through three diverse scenarios, including metallic mannequins, calibration spheres, and a complex multi-target environment containing human subjects and an Automated Guided Vehicle (AGV). The results demonstrate accurate 3D voxel-based reconstruction of targets even at reduced angular resolutions, advancing the feasibility of RIS-based sensing for industrial and security applications.