Researchers in UC Santa Barbara Professor Yasamin Mostofi’s lab have proposed a new foundation that can enable high-quality imaging of still objects with only Wi-Fi signals. Their method uses the Geometrical Theory of Diffraction and the corresponding Keller cones to trace edges of the objects.

The technique — which appeared in the Proceedings of the 2023 IEEE National Conference on Radar (RadarConf) — has also enabled, for the first time, imaging, or reading, the English alphabet through walls with Wi-Fi, a task once deemed too difficult for Wi-Fi due to the complex details of the letters.

“Imaging still scenery with Wi-Fi is considerably challenging due to the lack of motion,” said Mostofi. “We have then taken a completely different approach to tackle this challenging problem by focusing on tracing the edges of the objects instead.”

This innovation builds on previous work in the Mostofi Lab, which since 2009 has pioneered sensing with everyday radio frequency signals such as WiFi for several different applications, including crowd analytics, person identification, smart health, and smart spaces.

“The core fundamental idea is focusing on tracing the edges of the objects, by using the Geometrical Theory of Diffraction (GTD),” Mostofi told Tech Briefs in an exclusive interview, the entirety of which can be read below. “When a given wave is incident on an edge point, a cone of outgoing rays emerges according to the GTD, referred to as a Keller cone. For a given incident wave, the cone’s shape and orientation change based on the orientation of the edge.

From left to right: Ph.D. student Anurag Pallaprolu; former Ph.D. student Belal Korany and Professor Yasamin Mostofi (Image: Courtesy Mostofi Lab)

“Thus, depending on the orientation of the edge, it will leave different footprints, formally known as conic sections, on a given receiver grid nearby. We have then developed a new mathematical framework that uses these conic footprints as signatures to infer the orientation of the edges, thus creating an edge map of the scene.”

The team has also extensively studied the impact of several different parameters, such as curvature of a surface, edge orientation, distance to the receiver grid, and transmitter location on the Keller cones and their proposed edge-based imaging system, thereby developing a foundation for a methodical imaging system design.

In the team’s experiments, three off-the-shelf Wi-Fi transmitters send wireless waves in the area. Wi-Fi receivers are then mounted on an unmanned vehicle that emulates a Wi-Fi receiver grid as it moves. The receiver measures the received signal power which it then uses for imaging, based on the proposed methodology.

The researchers have extensively tested this technology with several experiments in three different areas, including through-wall scenarios. In one example application, they developed a Wi-Fi Reader to showcase the capabilities of the proposed pipeline.

Overall, the proposed approach can open up new directions for RF imaging.

Here is the Tech Briefs interview — edited for length and clarity — with Mostofi.

Tech Briefs: I’m sure there were too many to count, but what was the biggest technical challenge you faced while developing this imaging system?

Mostofi: That is a great question. My lab has been working on sensing with Wi-Fi signals since 2009, with our first imaging result with Wi-Fi officially published in a 2010 paper. Since then, we have enabled many different applications with Wi-Fi from imaging still objects, and crowd analytics, to smart health, and person identification.

However, while we have shown good progress over the years, imaging still objects with Wi-Fi has remained the most challenging, due to the lack of motion, as compared to the other applications. This was then the main motivation for this paper, in which we took a completely different approach to this problem by focusing on the edges of the objects.

Tech Briefs: What’s the next step? Do you have any plans for further research?

Mostofi: Our next step for this research is to enable imaging of even more complex scenes with Wi-Fi.

We have focused on imaging objects that have intricate details to showcase the potentials of the proposed approach. This was the main motivation for imaging the English alphabet as they have complex details. In addition, we have imaged a number of household items that have intricate details.

Tech Briefs: What kind of new directions could this lead to?

Mostofi: Imaging still objects is important for many RF sensing applications as it is key to context inference and scene understanding. In addition, it can find applications in smart home, smart spaces, structural health monitoring, search and rescue, surveillance, and excavation domains.

Tech Briefs: Do you have any advice for researchers aiming to bring their ideas to fruition?

Mostofi: Getting an idea to fruition not only requires many hours of hard work but will also involve not getting discouraged by the setbacks that one would naturally face. That is what I tell my lab members: to be bold, keep at it, do the hard work, and most importantly enjoy the process.

Tech Briefs: Is there anything else you’d like to add?

Mostofi: I would like to acknowledge my Ph.D. student Anurag Pallaprolu as well as now-graduated Ph.D. student Belal Korany, who have worked very hard on this project.