RF-Vision: Object Characterization Using Radio Frequency Propagation in Wireless Digital Twin

In today's rapidly evolving technological landscape, accurate object characterization is crucial for a wide range of applications, from autonomous systems to smart environments and security. Simultaneously, the growing concern for privacy necessitates innovative approaches that can characterize objects without compromising sensitive visual information. In this paper, we introduce a novel approach for object characterization using Radio Frequency (RF) propagation map generation through ray-tracing within a Digital Twin (DT) framework. We outline a systematic pipeline for leveraging NVIDIA's Sionna RayTracing tool to generate DT propagation maps created in Blender for indoor environments. Using these propagation maps, we propose a machine learning-based approach to facilitate object characterization. Our results demonstrate the feasibility of object characterization through strategic scene configuration using a small dataset that leverages RF maps within DTs. This paper provides valuable insights into the potential of our framework as a reliable and more efficient method for object characterization, offering a promising alternative to traditional vision-based techniques in scenarios where privacy concerns or environmental constraints limit the use of conventional imaging methods.