In this paper, we are interested in passively tracking multiple people walking in an area, using only the magnitude of WiFi signals from one WiFi transmitter and a small number of receivers (configured as an array) located on one side of the area. Past works on RF-based tracking either track only a single moving person, use a large number of transceivers surrounding the area to track multiple people, or use additional resources like ultra-wideband signals. Furthermore, magnitude-based tracking provides an attractive feature that additional receiver antennas can easily be added to the antenna array as needed, without the need for phase synchronization, since the magnitude can be measured independently on the different antennas. In this paper, we then propose a new framework that uses only the magnitude of WiFi signals and expresses it in terms of the angles of arrival of signal paths at the receivers as well as the motion parameters of the virtual arrays emulated by the moving people. We then use a two-dimensional MUltiple SIgnal Classification (MUSIC) algorithm to estimate the aforementioned parameters, and further utilize a Particle Filter with a Joint Probabilistic Data Association Filter to track multiple people walking in the area. We extensively validate our proposed framework in both indoor and outdoor areas, through 40 experiments of tracking 1 to 3 people, using only one transmit antenna and three laptops as receivers (a total of four off-the-shelf Intel 5300 WiFi Network Interface Cards (NICs)). Our results show highly accurate tracking (mean error of 38 cm in outdoor areas/closed parking lots, and 55 cm in indoor areas) using minimal WiFi resources on only one side of the area.

In this paper, we propose XModal-ID, a novel WiFi-video cross-modal gait-based person identification system. Given the WiFi signal measured when an unknown person walks in an unknown area and a video footage of a walking person in another area, XModal-ID can determine whether it is the same person in both cases or not. XModal-ID only uses the Channel State Information (CSI) magnitude measurements of a pair of off-the-shelf WiFi transceivers. It does not need any prior wireless or video measurement of the person to be identified. Similarly, it does not need any knowledge of the operation area or person's track. Finally, it can identify people through walls. XModal-ID utilizes the video footage to simulate the WiFi signal that would be generated if the person in the video walked near a pair of WiFi transceivers. It then uses a new processing approach to robustly extract key gait features from both the real WiFi signal and the video-based simulated one, and compares them to determine if the person in the WiFi area is the same person in the video. We extensively evaluate XModal-ID by building a large test set with $8$ subjects, $2$ video areas, and $5$ WiFi areas, including 3 through-wall areas as well as complex walking paths, all of which are not seen during the training phase. Overall, we have a total of 2,256 WiFi-video test pairs. XModal-ID then achieves an $85%$ accuracy in predicting whether a pair of WiFi and video samples belong to the same person or not. Furthermore, in a ranking scenario where XModal-ID compares a WiFi sample to $8$ candidate video samples, it obtains top-1, top-2, and top-3 accuracies of $75%$, $90%$, and $97%$. These results show that XModal-ID can robustly identify new people walking in new environments, in various practical scenarios.

In this paper, we are interested in estimating the angle of arrival (AoA) of all the signal paths arriving at a receiver array using only the corresponding received signal magnitude measurements (or, equivalently, the received power measurements). Typical AoA estimation techniques require phase information, which is not available in some WiFi/Bluetooth receivers, and is further challenging to properly measure in a synthetic antenna array due to synchronization issues. In this paper, we then show that AoA estimation is possible with only the received signal magnitude measurements. More specifically, we first propose a framework, based on the spatial correlation of the received signal magnitude, to estimate the AoA of signal paths from fixed signal sources (both active transmitters and passive objects). Next, we extend our AoA estimation framework to a dual setting, and further utilize a particle filter, to show how a moving target (both active transmitters and passive robots/humans) can be tracked, based on only the received signal magnitude measurements of a small number of fixed receivers. We extensively validate our proposed framework with several experiments (total of 22), in both closed and open areas. More specifically, we first utilize a robot to emulate an antenna array, and estimate the AoA of active transmitters, as well as passive objects using only the received WiFi signal magnitude measurements. We next validate our tracking framework by using only three off-the-shelf WiFi devices as receivers, to track an active transmitter, a passive robot that writes the letters of IPSN on its path, and a walking human. Overall, our results show that AoA can be estimated, with a high accuracy, with only the received signal magnitude measurements, and can be utilized for high quality angular localization and tracking.

In this paper, we are interested in the 3D through-wall imaging of a completely unknown area, using WiFi RSSI and Unmanned Aerial Vehicles (UAVs) that move outside of the area of interest to collect WiFi measurements. It is challenging to estimate a volume represented by an extremely high number of voxels with a small number of measurements. Yet many applications are time-critical and/or limited on resources, precluding extensive measurement collection. In this paper, we then propose an approach based on Markov random field modeling, loopy belief propagation, and sparse signal processing for 3D imaging based on wireless power measurements. Furthermore, we show how to design efficient aerial routes that are informative for 3D imaging. Finally, we design and implement a complete experimental testbed and show high-quality 3D robotic through-wall imaging of unknown areas with less than 4% of measurements.