Scientists trace objects behind a wall using WiFi signals
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WiFi can read through the walls. A team of researchers has developed a new method that can replicate the image of a still object from behind a wall using WiFi. Does this mean we can spy on our neighbors next door? Quite unlikely.
Sensing moving things using WiFi signals has shown promise; there’s a challenge in doing the same for non-moving objects because they lack motion, enabling calls to track them, explain the researchers in the press release.
To overcome this challenge, researchers used still objects - English alphabets - to trace their edges. Their approach, Wiffract, uses the radio waves of off-the-shelf WiFi transceivers to carry out this experiment.
How did they do it?
Wiffract was developed using Joseph Keller’s Geometrical Theory of Diffraction (GTD), which exploits signatures that edges leave on the receiver grid. When a wave strikes an edge, a cone of outgoing waves emerges, referred to as a Keller cone, according to the GTD. This interaction not only applies to visibly sharp edges but also to all surfaces. The researchers installed a received grid in the vicinity of the edge. The rays leave different signatures on the receiver grid, which the researchers have used to define the image they were tracking.
“We then develop a mathematical framework that uses these conic footprints as signatures to infer the orientation of the edges, thus creating an edge map of the scene,” said Yasamin Mostofi, UC Santa Barbara professor.
This enabled the first demonstration of WiFi imaging (or reading) the English alphabet through walls.
Writings on the wall
The team placed the letters of the word "BELIEVE" behind a wall (one by one) for the WiFi to read. The final imaging results for the term showed that it was imaged well. “Not only is it easy to identify the letters, but also the details of the letters are imaged well. Overall, Wiffract has enabled WiFi to read through walls for the first time,” said the researchers.
The team ran 30 experiments for imaging uppercase English letters. Their approach was able to capture the details of the letters pretty well.
Once the image is formed, the researchers can further improve the appearance by using image completion tools from the area of vision. Different applications of Wiffract include crowd analytics, person identification, innovative health, and smart spaces.
“It is worth noting that traditional imaging techniques result in poor imaging quality when deployed with commodity WiFi transceivers as the surfaces can appear near-specular at lower frequencies, thus not leaving enough signature on the receiver grid,” said Anurag Pallaprolu, the lead Ph.D. student on the project.
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