WiCamera: Vortex Electromagnetic Wave-Based WiFi Imaging

Current WiFi imaging approaches focus on monitoring dynamic targets to facilitate easy object distinction and capture rich signal reflections for image construction. In static object imaging, massive antenna array or emulated antenna array is often necessary. We propose <i>WiCamera</i>, a novel WiFi imaging prototype that utilizes vortex electromagnetic waves (VEMWs) to monitor stationary human postures using commodity WiFi, by generating human silhouettes with only <inline-formula><tex-math notation="LaTeX">$3 \times 3$</tex-math></inline-formula> MIMO. VEMWs possess a helical wavefront with different phase variations, enabling the imaging of stationary objects through different OAM (Orbital Angular Momentum) modes with time-division multiplexing. <i>WiCamera</i> emits three OAM modes waves from WiFi devices and utilizes their phase variations for imaging. By ray tracing the received signals to a target image plane, <i>WiCamera</i> generates a wavefront image. A generative adversarial network (GAN)-based model is further utilized to refine the wavefront image and create a high-resolution human silhouette. The system's output images are evaluated using metrics such as structural similarity index measure (SSIM) and Szymkiewicz-Simpson coefficient (SSC), comparing them to ground truth images captured by cameras. The evaluation shows that <i>WiCamera</i> performs consistently well in various environments and with different users, with an SSIM reaching up to 0.89 and an SSC reaching up to 0.93.