Wiffract

Abstract

In this paper, we are interested in high-quality imaging of still objects with only received power measurements of off-the-shelf WiFi transceivers. We show that the scattered WiFi signals off of objects carry much richer information about the edges of the objects than the surface points. Based on this observation, we then propose a completely different way of thinking about this imaging problem. More specifically, we propose Wiffract, a new foundation for imaging objects via edge tracing. Our approach uses the Geometrical Theory of Diffraction (GTD) and the corresponding Keller cones to image edges of the object. We extensively validate our approach with 37 experiments in three different areas, including through-wall scenarios. We take developing a WiFi Reader as one example application to showcase the capabilities of our proposed pipeline. More specifically, we show how our approach can successfully image several alphabet-shaped objects. We further show that our approach enables WiFi to read, i.e., correctly classify the letters, with an accuracy of 86.7%. Finally, we show how our approach enables WiFi to image and read through walls, by imaging the details and further reading the letters of the word "BELIEVE" through walls. Overall, our proposed approach can open up new directions for RF imaging.