Thursday, April 18, 2024

Imaging still objects with only Wi-Fi signals

Wi-Fi technology has gained considerable traction in recent years with many potential applications. Imaging still objects or scenes with RF signals, however, is still considerably challenging due to the lack of motion.

Researchers in UC Santa Barbara professor Yasamin Mostofi’s lab have proposed a new method that can enable high-quality imaging of still objects with only Wi-Fi signals.

By using the Geometrical Theory of Diffraction and Keller cones, the new technique can trace the edges of objects, producing clear images. Moreover, the technique enables, for the first time, imaging or reading the English alphabet through walls with Wi-Fi, a task 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, a professor of electrical and computer engineering. “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 Wi-Fi for several different applications, including crowd analytics, person identification, smart health, and smart spaces.

“When a given wave is incident on an edge point, a cone of outgoing rays emerges according to Keller’s Geometrical Theory of Diffraction (GTD), referred to as a Keller cone,” Mostofi explained. The researchers note that this interaction is not limited to visibly sharp edges but applies to a broader set of surfaces with a small enough curvature.

The proposed Keller cone-based imaging projection kernel is implicitly a function of the edge orientations, a relationship that is then exploited to infer the existence/orientation of the edges via hypothesis testing over a small set of possible edge orientations. In other words, if the existence of an edge is determined, the edge orientation that best matches the resulting Keller cone-based signature is chosen for a given point that they are interested in imaging.

During the experiments, the team utilized three off-the-shelf Wi-Fi transmitters to emit wireless waves in the designated area. To emulate a Wi-Fi receiver grid, Wi-Fi receivers were mounted on an unmanned vehicle that moved around the area. The receiver measures the received signal power, which it then uses for imaging based on the proposed methodology.

The technology has undergone rigorous testing through various experiments in three different areas, one of which includes through-wall scenarios. In one example application, they developed a Wi-Fi Reader to showcase the capabilities of the proposed pipeline.

This research application is highly informative as it focuses on the complexities of the English alphabet and how they can be used to test the imaging system’s performance. The team has successfully imaged several alphabet-shaped objects and has even been able to further classify them.

Moreover, they have demonstrated the capability of Wi-Fi to image and read through walls by imaging the details and reading the letters of the word “BELIEVE” through walls. The team has also imaged a variety of other objects, showcasing their ability to capture previously unattainable details with Wi-Fi.

According to researchers, the proposed approach can open up new directions for RF imaging.