WiFi signals are everywhere. Unmanned aerial vehicles (UAVs, or drones) are expected to become a larger part of everyday life. A new methodology was developed for high-resolution, 3D, through-wall imaging of completely unknown areas, using only WiFi signals and UAVs. The approach utilizes only WiFi received signal strength measurements, does not require prior measurements in the area of interest, and the objects do not have to move to be imaged.
In experiments, two autonomous octo-copters take off and fly outside an enclosed, four-sided brick house whose interior is unknown to the drones. While in flight, one copter continuously transmits a WiFi signal, the received power of which is measured by the other copter for 3D imaging. After traversing a few proposed routes, the copters utilize the imaging methodology to reveal the area behind the walls and generate 3D high-resolution images of the objects inside. The 3D image closely matches the actual area. Figure 1 shows two examples of the UAVs in action.
This development builds on previous work by the researchers in sensing and imaging with everyday radio frequency signals such as WiFi. While a previous 2D method utilized ground-based robots working in tandem, the success of the 3D experiments is due to the copters’ ability to approach the area from several angles, as well as to the new proposed methodology.
The approach to enabling the 3D through-wall imaging utilizes four tightly integrated key components. First, robotic paths were proposed that can capture the spatial variations in all three dimensions as much as possible, while maintaining the efficiency of the operation. Second, the 3D unknown area of interest was modeled as a Markov Random Field to capture the spatial dependencies, and a graph-based belief propagation approach was used to update the imaging decision of each voxel (the smallest unit of a 3D image) based on the decisions of the neighboring voxels. Third, in order to approximate the interaction of the transmitted wave with the area of interest, a linear wave model was utilized.
Finally, the researchers took advantage of the compressibility of the information content to image the area with a very small number of WiFi measurements (less than 4 percent). Figure 2 summarizes the experimental testbed. The transmitting UAV (TX UAV) is equipped with a WiFi router for WiFi transmission, and a Google Tango tablet for localizing itself. Similarly, the receiver UAV (RX UAV) has a Raspberry Pi unit, a WiFi card for measuring the WiFi RSSI, and a Tango tablet for localizing itself. The autonomous UAVs have waypoints to achieve along their routes, and will constantly coordinate with each other so that they are at the right transmit/receive position pairs.