RF Sensing and Through-Wall Imaging with Unmanned Vehicles and WiFi
Chitra Karanam, Belal Korany, and Yasamin Mostofi, UCSB
|Angle of Arrival estimation with only signal magnitude is possible and has enabled passive tracking with WiFi signal magnitude of only a small number of transceivers (e.g., 4), only from one side of the area, [IPSN 18 paper for tracking one person] [IPSN 19 paper for tracking multiple people].||Video (from 2017) on first demonstration of 3D Imaging through walls with WiFi and drones [paper][project page]||Video (from 2014) on two ground vehicles imaging through walls in 2D with only WiFi [project page][most recent paper]|
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WiFi signals are everywhere these days. Unmanned aerial vehicles are expected to become a part of our near-future society. In this research, we are interested in the possibilities created at this intersection of robotics and communications. For instance, imagine unmanned vehicles arriving behind thick concrete walls. They have no prior knowledge of the area behind these walls. But they are able to see every square inch of the invisible area through the walls, fully discovering what is on the other side with a high accuracy. The objects on the other side do not even have to move to be detected. Now, imagine robots doing all these with only WiFi RSSI signals and no other sensors.
Our lab has been working on this problem of imaging through walls with WiFi signals since 2008. Here are some highlights of our work in this area. In IEEE ACC 2009, we proposed our initial approach for imaging with WiFi received power measurements (RSSI) and unmanned vehicles, and showed the first experimental demonstration of imaging with WiFi in IEEE Milcom 2010. The approach is based on devising robotic paths (TX/RX antenna positioning) that are most informative for robotic imaging, approximated wave modeling, and sparse signal processing. It is noteworthy that our approach only uses WiFi RSSI measurements and further does not rely on making prior measurements in the area of interest. In IEEE TMC 2012 (DOI: 10.1109/TMC.2012.32 for 2012 online publication) we then showed the first demonstration of imaging through walls with WiFi and unmanned vehicles. The 2012 Ph.D. Thesis of A. Gonzalez-Ruiz from our lab also contains several key findings and experimental results for see-through imaging. In IEEE Sensors Journal 2013, we then showed the tradeoffs between using different robotic paths and imaging quality. The IEEE Sensors Journal 2014 further shows how WiFi and laser scanners can be integrated to image more extensive areas through walls, using binary compressive sensing. The 2015 IEEE TVT paper has an extended method that can image more complex areas, based on using Rytov wave models. For a summary of robotic through-wall imaging, see our 2017 IEEE Antenna and Propagation Magazine paper. Our project page for 2D imaging through walls also has more details on the methodologies and results.
More recently, we are excited about our new approach that has enabled the first demonstration of 3D imaging through walls with only WiFi RSSI and drones in IPSN 2017 (See the video and the project page). There are four tightly-integrated key components to our proposed approach to enable 3D through-wall imaging. First, we have proposed robotic paths that can capture the spatial variations in all the three dimensions as much as possible while maintaining the efficiency of the operation. Second, we have modeled the 3D unknown area of interest as a Markov Random Field and utilized Loopy Belief Propagation to update the imaging decision of each voxel. In order to approximate the interaction of the transmitted wave with the area of interest, we have used the WKB linear wave model. Finally, we have also taken advantage of the compressibility of the information content to image the area with a very small number of WiFi measurements (less than 4%), using sparse signal processing. See the video and the project page for more details.
In our IPSN 2018 paper, we have shown that Angle of Arrival (AoA) estimation is possible with only signal magnitude. We have then shown how this can enable new possibilities for tracking of passive objects/humans with only WiFi signal magnitude measurements of a very small number of transceivers (e.g., 4) that are located only on one side of the area. In our IPSN 2019 paper, we have then extended this to tracking multiple people.
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C. R. Karanam, B. Korany, and Y. Mostofi, "Tracking from One Side - Multi-Person Passive Tracking with WiFi Magnitude Measurements," in proceedings of the 18th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), April 2019 (acceptance rate: 27%).[pdf][bibtex]
C. R. Karanam*, B. Korany*, and Y. Mostofi, "Magnitude-Based Angle-of-Arrival Estimation, Localization, and Target Tracking," in proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), April 2018 (acceptance rate: 26.5%).[pdf][bibtex] (*equal contribution) (Angle of Arrival Estimation (AoA) with Only Signal Magnitude is Possible)
B. Korany*, C. R. Karanam*, and Y. Mostofi, "Adaptive Near-Field Imaging with Robotic Arrays," in proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), July 2018.[pdf]
B. Korany, S. Depatla, and Y. Mostofi, "Subspace-Based Imaging Using Only Power Measurements," in proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), July 2018.[pdf]
C. R. Karanam and Y. Mostofi, "3D Through-Wall Imaging with Unmanned Aerial Vehicles Using WiFi," in the proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), April 2017.[pdf][bibtex] (Our New Methodology that Has Enabled the First Demonstration of 3D Through-Wall Imaging with WiFi)
S. Depatla, C. R. Karanam, and Y. Mostofi, "Robotic Through-Wall Imaging," IEEE Antenna and Propagation Magazine, Special issue on Electromagnetic Inverse Problems for Sensing and Imaging, August 2017.[pdf][bibtex] (Overview of Robotic Through-Wall Imaging, Comparison of WiFi and UWB for Through-Wall Imaging)
S. Depatla, L. Buckland, and Y. Mostofi, "X-Ray Vision with Only WiFi Power Measurements Using Rytov Wave Models," IEEE Transactions on Vehicular Technology, special issue on Indoor Localization, Tracking, and Mapping, volume 64, issue 4, pp. 1376-1387, April 2015.[pdf][bibtex] ( See-Through Imaging of More Complex Areas)
A. Gonzalez-Ruiz, A Ghaffarkhah, and Y. Mostofi, "An Integrated Framework for Obstacle Mapping with See-Through Capabilities using Laser and Wireless Channel Measurements," IEEE Sensors Journal volume 14, issue 1, Jan. 2014.[pdf][bibtex] (Integration of WiFi and Laser Scanner for Robotic Imaging)
A. Gonzalez-Ruiz and Y. Mostofi, "Cooperative Robotic Structure Mapping Using Wireless Measurements - A Comparison of Random and Coordinated Sampling Patterns," IEEE Sensors Journal, volume 13, issue 7, April 2013.[pdf][bibtex] (Comparison of Different Robotic Paths for See-Through Imaging)
Ph.D. Thesis: A. Gonzalez-Ruiz, "Compressive Cooperative Obstacle Mapping with See-Through Capabilities in Mobile Networks," Dec. 2012.[pdf][bibtex] (Advisee PhD Thesis on See-Through Imaging with WiFi RSSI)
Y. Mostofi, "Cooperative Wireless-Based Obstacle/Object Mapping and See-Through Capabilities in Robotic Networks," IEEE Transactions on Mobile Computing, DOI: 10.1109/TMC.2012.32, January 2012.[pdf][bibtex] (First Demonstration of See-Through Imaging with WiFi RSSI)
Y. Mostofi, "Compressive Cooperative Sensing and Mapping in Mobile Networks," IEEE Transactions on Mobile Computing, vol. 10, no. 12, pp. 1770-1785, December 2011.[pdf][bibtex] (More Imaging Results with WiFi RSSI)
Y. Mostofi and A. Gonzalez-Ruiz, "Compressive Cooperative Obstacle Mapping in Mobile Networks," invited paper, IEEE Military Communications Conference (Milcom), Oct. 2010. [pdf][bibtex] (First Experimental Demonstration of Imaging with WiFi RSSI)
Y. Mostofi and P. Sen, "Compressive Cooperative Mapping in Mobile Networks," American Control Conference (ACC), 2009. [pdf][bibtex] (Initial Proposed See-Through Imaging Approach with WiFi RSSI Signals)
Patent: Y. Mostofi and P. Sen, "System and methods for obstacle mapping and navigation," patent # 8,712,679, 2014.
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Dept. of ECE and College of Engineering at UCSB for facilitating the experiments.