WiFi-equipped drones extract through-wall 3D-maps out of RSSI measurements

June 29, 2017 // By Julien Happich
WiFi-equipped drones extract through-wall 3D-maps out of RSSI measurements
In a recent paper presented at last ACM/IEEE International Conference on Information Processing (IPSN), researchers from the University of California Santa Barbara demonstrated how using two drones flying in tandem, one carrying a WiFi transmitter and the other a WiFi receiver, fly paths could be optimized so as to efficiently reconstruct a 3D image of a walled-up or occluded area, based on WiFi's Received Signal Strength Indicator (RSSI) measurements alone.

Such 3D through-wall imaging would be very useful in disaster scenarios for search and rescue missions, or for surveillance operations.

In their paper titled "3D Through-Wall Imaging with Unmanned Aerial Vehicles Using WiFi", the researchers note that although through-wall imaging based on WiFi's RSSI measurements had already been successfully demonstrated in 2D using unmanned ground vehicles (but only yielding a 2D footprint), moving up to the third dimension was much more challenging, especially with the constraint to estimate rapidly a high number of voxels (the volume) from only a relatively small number of measurements.

(left) An L-shaped brick construction measuring 2.96x2.96x0.5m, (middle) its 3D binary ground-truth image, and (right) the reconstructed 3D binary image based on RSSI measurements.

In search and rescue situations, time is of essence, hence limiting the number of measurement paths and making 3D-reconstruction more difficult based on how the WiFi signal is being affected by the obstacles within the unknown area.

The researchers solved this problem using Markov random field (MRF) modelling, loopy belief propagation and sparse signal processing, together with optimized path planning to fly the drones (two octo-copters) along zigzagging parallel 3D routes, on each side of the walled-up area.

With their experiments, the researchers demonstrated how they were able to obtain high-quality through-wall images of different test setups made up of thick brick walls, with less than 4% of measurements.


University of California Santa Barbara - www.ucsb.edu

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