Exploiting radio frequency signals is promising for locating and tracking objects. Prior works focus on per-tag localization, in which each object is attached with one tag. In this paper, we propose a comprehensive localization and tracking scheme by attaching two RFID tags to one object. Instead of using per-tag localization pattern, adding one-more RFID tag to the object exhibits several benefits: 1) providing rich freedom in RFID reader's antenna spacing and placement; 2) supporting accurate calibration of the reader's antenna location and spacing, and 3) enabling fine-grained calculation on the orientation of the tags. All of these advantages ultimately improve the localization/tracking accuracy. Our extensive experimental results demonstrate that the average errors of localization and orientation of target tags are 6.415 cm and 1.330°, respectively. Our results also verify that the reader's antenna geometry does have impact on tag positioning performance.

Contact-free vital sign monitoring, which uses wireless signals for recognizing human vital signs (i.e, breath and heartbeat), is an attractive solution to health and security. However, the subject’s body movement and the change in actual environments can result in inaccurate frequency estimation of heartbeat and respiratory. In this paper, we propose a robust mmWave radar and camera fusion system for monitoring vital signs, which can perform consistently well in dynamic scenarios, e.g., when some people move around the subject to be tracked, or a subject waves his/her arms and marches on the spot. Three major processing modules are developed in the system, to enable robust sensing. First, we utilize a camera to assist a mmWave radar to accurately localize the subjects of interest. Second, we exploit the calculated subject position to form transmitting and receiving beamformers, which can improve the reflected power from the targets and weaken the impact of dynamic interference. Third, we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD) algorithm to separate the weak vital sign signals from the dynamic ones due to subject’s body movement. Experimental results show that, the 90th percentile errors in respiration rate (RR) and heartbeat rate (HR) are less than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute), respectively.

Device-free human tracking is an essential ingredient for ubiquitous wireless sensing. Recent passive WiFi tracking systems face the challenges of inaccurate separation of dynamic human components and time-consuming estimation of multi-dimensional signal parameters. In this work, we present a scheme named Wi Fi D oppler F requency S hift ( WiDFS ), which can achieve single-target real-time passive tracking using channel state information (CSI) collected from commercial-off-the-shelf (COTS) WiFi devices. We consider the typical system setup including a transmitter with a single antenna and a receiver with three antennas; while our scheme can be readily extended to another setup. To remove the impact of transceiver asynchronization, we first apply CSI cross-correlation between each RX antenna pair. We then combine them to estimate a Doppler frequency shift (DFS) in a short-time window. After that, we leverage the DFS estimate to separate dynamic human components from CSI self-correlation terms of each antenna, thereby separately calculating angle-of-arrival (AoA) and human reflection distance for tracking. In addition, a hardware calibration algorithm is presented to refine the spacing between RX antennas and eliminate the hardware-related phase differences between them. A prototype demonstrates that WiDFS can achieve real-time tracking with a median position error of 72.32 cm in multipath-rich environments.

Capturing RFID tags in the region of interest (ROI) is challenging. Many issues, such as multipath interference, frequency-dependent hardware characteristics and phase periodicity, make RF phase difficult to accurately indicate the tag-to-antenna distance for RFID tag localization. In this paper, we propose a comprehensive solution, called RF-Focus, which fuses RFID and computer vision (CV) techniques to recognize and locate moving RFID-tagged objects within ROI. Firstly, we build a multipath propagation model and propose a dual-antenna solution to minimize the impact of multipath interference on RF phase. Secondly, by extending the multipath model, we estimate phase shifts due to hardware characteristics at different operating frequencies. Thirdly, to minimize the tag position uncertainty due to RF phase periodicity, we leverage CV to extract image regions of being likely to contain ROI RFID-tagged objects, and then associate them with the processed RF phase after the removal of the phase shifts due to multipath interference and hardware characteristics for recognition and localization. Our experiments demonstrate the effectiveness of multipath modelling and hardware-related phase shift estimation. When five RFID-tagged objects are moving in the ROI, RF-Focus achieves the average recognition accuracy of 91.67% and localization accuracy of 94.26% given a false positive rate of 10%.

Falling is one of the major health threats to the independent living of elders, and falling has received much attention in academia and industry over the past two decades. Previous fall detection methods either require specialized hardware or invade people's daily lives. These limitations make it difficult to widely deploy fall detection systems in houses. On the other hand, a long static case is also one of the health threats to the elderly; these cases indicate that something is abnormal. In this paper, we present the design and implementation of a motion detection system based on passive radio frequency identification tags. The key finding is that static, regular action, and sudden falls make an impact on the RSS and Doppler frequency values differently. Such features help us to detect the status of the elderly. A wavelet transform is used to pre-process the signal data, and a support vector machine is adopted to improve the accuracy of the fall detection. We implemented a prototype monitoring system called TagCare and conducted extensive experiments to demonstrate the accuracy and efficiency of TagCare in movement detection and fall behavior identification for the elderly.