Dec 14 (Mon) @ 12:00pm: "Robotic RF Sensing with Off-the-Shelf Devices," Chitra Karanam, ECE PhD Defense
Radio Frequency (RF) signals, like WiFi, are ubiquitous in our surroundings. These signals interact with the objects as well as people, on their path from the transmitter to the receiver, and can thus carry implicit information about the area that they pass through. Sensing and extracting such information from off-the-shelf devices in our surroundings, is a problem of considerable interest. Additionally, robots are becoming an integral part of our lives. Utilizing the mobility of robots, along with the ubiquity of off-the-shelf devices, opens up new possibilities for RF sensing with robots. In this talk, I will show how we can use readily available off-the-shelf devices to deduce information about our surroundings, and discuss various possibilities for utilizing the mobility of robots for RF sensing.
First, I will discuss how we can use WiFi RSSI measurements and drones to achieve 3D through-wall imaging of completely unknown areas behind thick brick walls. This is possible through our proposed approach involving signal propagation modeling, sparsity and spatial correlation exploitation, and path planning optimization. I will then present our experimental results obtained using our extensive testbed that includes drones and off-the-shelf WiFi devices. In the second part of the talk, I will introduce our new approach to the traditional angle-of-arrival (AoA) estimation problem, which enables AoA estimation with only the signal magnitude at an antenna array, and without the need for signal phase measurements. I will discuss our proposed framework, based on the spatial correlation of the signal magnitude, and discuss the fundamental ambiguities that can arise in such a framework and ways to address them. I will then show how this new framework allows for predicting the ray makeup, and the resulting channel quality, at unvisited locations in the workspace.
Finally, I will discuss our proposed approach for multi-target tracking using WiFi. Here, our approach has enabled passive tracking of multiple people walking in an area, with a small number of transceivers located on one side of the area, and without the need for the people to carry any device. I will discuss our approach that builds on the magnitude-based AoA framework, and utilizes multi-dimensional parameter extraction and particle filtering in order to track multiple targets. I will then present our extensive experimental results for passively tracking up to three simultaneously walking people, using a small number of WiFi transceivers on one side of the area.
Chitra Karanam received her B.Tech and M.Tech dual degree in Electrical Engineering from the Indian Institute of Technology Madras in 2014, and her M.S. degree in Electrical and Computer Engineering from the University of California, Santa Barbara in 2016. She is currently a Ph.D. candidate in the ECE department at UCSB and is advised by Professor Yasamin Mostofi. Her research interests include robotic RF sensing, wireless localization, and array signal processing.
Hosted by: Professor Yasamin Mostofi
Submitted by: Chitra Karanam <firstname.lastname@example.org>