We continue our series of Hardware related compressive sensing blog entries with this tomography using WiFi and robots to cover the area of interest from different angles.
X-Ray Vision with Only WiFi Power Measurements Using Rytov Wave Models by Saandeep Depatla, Lucas Buckland and Yasamin Mostofi
In this paper, unmanned vehicles are tasked with seeing a completely unknown area behind thick walls based on only wireless power measurements using WLAN cards. We show that a proper modeling of wave propagation that considers scattering and other propagation phenomena can result in a considerable improvement in see-through imaging. More specifically, we develop a theoretical and experimental framework for this problem based on Rytov wave models, and integrate it with sparse signal processing and robotic path planning. Our experimental results show high-resolution imaging of three different areas, validating the proposed framework. Moreover, they show considerable performance improvement over the state-of-the-art that only considers the Line Of Sight (LOS) path, allowing us to image more complex areas not possible before. Finally, we show the impact of robot positioning and antenna alignment errors on our see-through imaging framework.
In the end, I note their use of TV as a way to regularize their signals and I wonder if by modulating the power level of the emitters and having more than one receivers (they seem to have only two robots, one emitting the other receiving), then the results might be even more beautiful than what they have. While the system is indeed linear, it seems to rely on much trajectory design for both robots. With additional receivers, one would think that some of the trajectory issues might become less important but then additional receivers woud also mean that the system is not linear anymore as is the case of CT (traditional CT is linear, source coding CT is not).
On a totally unrelated note, I wonder if using monocular camera SLAM would simplify some of the embedded coding that goes into this current set up.
The project page is here.
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