Towards Real-Time Fine-Grained Tracking in Distributed Large-Scale RF Tag Systems

Project Information

  • Project period: January 1, 2023 – December 31, 2025 (Estimated)

Project Overview

A scalable wireless system that can continuously, accurately, and simultaneously track multiple people's movements and detect their fine-grained body motions will enable the development of many novel real-life applications, such as diagnosing movement disorders, continuously monitoring children's behaviors, and alerting caregivers of elderly falls in real time. The objective of this project is to design and implement such a wireless tracking system by employing radio frequency (RF) tag based wireless sensing technology. A team of researchers from Michigan State University will address the fundamental challenges in both theory and experiments through innovations in signal processing, mathematical modeling, algorithm and protocol design, machine learning, optimization, radar design, and harmonic tag fabrication. The wireless tracking system will make it possible to monitor the activities of elderly residents in nursing homes while preserving their privacy, predict and prevent their possible falls, and guide them to perform doctor-suggested exercises. It will also enable the continuous monitoring of children's behaviors and activities in a non-invasive manner, making it possible to conduct data-driven studies of children's physical and mental development.

Project Goal

The overarching goal of this project is to design and implement a scalable wireless tag tracking system that features large coverage area, distributed sensing, inter-reader coordination, simultaneous multi-tag tracking, and high resolution in time. It comprises three complementary research thrusts. The first thrust is to develop fine-grained tracking solutions for a large-scale tag tracking system by coordinating and synchronizing location-distributed radio frequency (RF) readers for precise localization. It will develop new techniques to maximize the time resolution of tag tracking through optimizing signal processing algorithms, inter-reader communication and coordination protocols, tag scheduling, and resource allocation. The second thrust is to develop techniques to enable simultaneous multi-tag tracking similar to the idea of multi-user MIMO (multiple-input and multiple-output) in cellular networks. It will significantly improve the time resolution of tag tracking, especially in tag-dense scenarios. The third thrust focuses on motion tracking using narrowband harmonic tags and micro-Doppler radar. It will advance the design and fabrication of harmonic RF tags and radars for frequency-selective multi-tag operations, and explore the performance limits of harmonic tags in tracking applications. In addition, this project includes a strong component of system implementation and experimental evaluation, with the ultimate goal of demonstrating its real-life applications.

Publications

  • mReader: Concurrent UHF RFID tag reading [PDF]
    H. Pirayesh*, S. Zhang*, and H. Zeng,
    ACM MobiHoc 2023. [Acceptance rate: 21.9%]

Research Accomplishments

  • Year 1 - Enabling concurrent UHF RFID tag reading using MU-MIMO technology: UHF RFID tags have been widely used for contactless inventory and tracking applications. One fundamental problem with RFID readers is their limited tag reading rate. Existing RFID readers (e.g., Impinj Speedway) can read about 35 tags per second in a read zone, which is far from enough for many applications. In this project, we developed the first-of-its-kind RFID reader (mReader), which borrows the idea of multi-user MIMO (MU-MIMO) from cellular networks to enable concurrent multi-tag reading in passive RFID systems. mReader is equipped with multiple antennas for implicit beamforming in downlink transmissions. It is enabled by three key techniques: uplink collision recovery, transition-based channel estimation, and zero-overhead channel calibration. In addition, mReader employs a Q-value adaptation algorithm for medium access control to maximize its tag reading rate. We have built a prototype of mReader on USRP X310 and demonstrated for the first time that a two-antenna reader can read two commercial off-the-shelf (COTS) tags simultaneously. Numerical results further show that mReader can improve the tag reading rate by 45% compared to existing RFID readers.

mreader

  • Year 1 - Compact harmonic tag design: Our initial narrowband harmonic tag design is based on a split-ring resonant antenna tuned to a narrow bandwidth that concentrically surrounds a circular patch antenna. The ring is tuned to the fundamental frequency f1 while the tag is designed to be resonant at the first harmonic 2f1. Between the split ring and the patch is a diode. When the ring receives the incident radiation at f1 and inputs the signal to the diode, a set of harmonic frequencies is generated. By tuning the patch to the second harmonic, only this frequency is retransmitted back to the interrogating system. From this retransmitted harmonic signal, detection and tracking of the tag is possible. We previously implemented this design at frequencies of 2.55 GHz. The design was scaled to a higher frequency (510 GHz) to reduce the size of the tag and explore its usage in a scalable format. We recently designed multiple tags at adjacent frequency bands, which will be experimentally measured in the coming months. We have also explored the use of a frequency subsampling radar receiver, which samples the received signal spectrum at a rate lower than the Nyquist rate. This causes the signals received from the tags to fold down to the first Nyquist zone, requiring significantly less bandwidth. Since the tags are narrowband, the received signals are essentially tones, with small modulation due to the motion of the tag. Because the transmit frequencies are known, they can be designed to avoid interference in the first Nyquist zone. The result is a lower-cost receiver that can detect tags at a wider range of carrier frequency bands.

  • Year 1 - Wireless network coordination: Wireless clock synchronization is important for accurate distributed processing. We are building a distributed receive processing system in software-defined radios (SDRs) that will leverage wireless frequency and time alignment. The frequency alignment approach is based on the transmission of a frequency reference that is double sideband modulated onto a carrier frequency and distributed between nodes. The receiving node captures the signals and demodulates the reference via a self-mixing circuit; the reference frequency is then used as the input to a phase-locked loop to synchronize the clocks. We are currently implementing a two-node SDR-based system and will evaluate the coordination accuracy and the ability to support cooperative transmission and reception.

Broader Impacts

  • Year 1 – PI Zeng has hosted undergraduate research on Bluetooth device tracking: This project hosted an undergraduate research project for Brendan Bushbaker (an undergraduate student in the Department of Computer Science at Michigan State University). He learned Bluetooth communication protocols and signal processing pipeline in this project. The developed a website to track any Bluetooth devices (e.g., iPhone, iPad, smart watch, and EarPods) in the proximity of our custom-designed sensors. In the website, end users can access the information of Bluetooth device location and tracking. The screenshot of the website from this undergraduate research project can be found below.

Bluetooth Website

  • Year 1 – PI Zeng has given talk on smart device tracking to undegraduate students at UDC (HBCU): PI Zeng gave a talk on “Location privacy risk of 5G smart devices” to the undergraduate students in the Department of Computer Science and Information Technology at the University of the District of Columbia, which is one of the Historically Black Colleges and Universities (HBCUs). During the talk, PI Zeng introduced the communication protocols and signal structure of smart devices such as smart watches, EarPods, phones, and smart glasses. Then, he demonstrated that without any modification on the smart devices, those smart devices can be tracked by leveraging the radio signals emitted by those devices. Students also learned how to use machine learning algorithms to improve the accuracy of device location and tracking in a large area, as well as the potential security threats poised by illegitimate tracking systems.

  • Year 1 – In this reporting period, Co-PI Nanzer has given talks presenting research supported by this effort at the following locations:

    • IEEE MTT/AP Syracuse Joint Chapter (virtual)

    • Dalhousie University (virtual)

    • James Cook University, Cairns Australia

    • University of Queensland, Brisbane Australia

    • University of Adelaide, Adelaide Australia

    • Royal Melbourne Institute of Technology, Melbourne Australia

    • University of Technology Sydney, Sydney Australia

    • Third MARIE IRTG Scientific Seminar (Keynote Speaker) (virtual)

    • The University of Colorado at Boulder, Boulder CO

    • The Ohio State University, Columbus OH

    • University of Texas at Dallas, Dallas TX

    • IEEE Texas Symposium on Wireless & Microwave Circuits and Systems, Waco TX

    • EuCAP workshop on phased array antennas, Florence Italy

    • University of Illinois at Urbana-Champaign, Urbana IL

    • IEEE MTT-S Kerala Section (virtual)

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