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Contact-free multitarget tracking using distributed Massive MIMO-OFDM communication system : prototype and analysis

Chenglong Li, Sibren De Bast, Yang Miao, Emmeric Tanghe (UGent) , Sofie Pollin and Wout Joseph (UGent)
(2023) IEEE INTERNET OF THINGS JOURNAL. 10(10). p.9220-9233
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Abstract
Wireless-based human activity recognition has become an essential technology that enables contact-free human-machine and human-environment interactions. In this article, we consider contact-free multitarget tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6-GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then, the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets' locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density (GM-PHD) filter for tracking. A multipedestrian tracking experiment is conducted in a room with a size of 6.5 mx10 m to evaluate the performance of the proposed algorithm. According to the experimental results, we achieve 75th and 95th percentile accuracy of 12.7 and 18.2 cm for single-person tracking and 28.9 and 45.7 cm for multiperson tracking, respectively. Furthermore, the proposed algorithm achieves tracking purposes in real time, which is promising for practical MTT use cases.
Keywords
OPPORTUNITIES, LOCALIZATION, Channel state information (CSI), indoor localization, integrated sensing and communication (ISAC), massive multiple-input and multiple-output (MIMO), multitarget tracking (MTT), radar

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Citation

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MLA
Li, Chenglong, et al. “Contact-Free Multitarget Tracking Using Distributed Massive MIMO-OFDM Communication System : Prototype and Analysis.” IEEE INTERNET OF THINGS JOURNAL, vol. 10, no. 10, 2023, pp. 9220–33, doi:10.1109/JIOT.2023.3234041.
APA
Li, C., De Bast, S., Miao, Y., Tanghe, E., Pollin, S., & Joseph, W. (2023). Contact-free multitarget tracking using distributed Massive MIMO-OFDM communication system : prototype and analysis. IEEE INTERNET OF THINGS JOURNAL, 10(10), 9220–9233. https://doi.org/10.1109/JIOT.2023.3234041
Chicago author-date
Li, Chenglong, Sibren De Bast, Yang Miao, Emmeric Tanghe, Sofie Pollin, and Wout Joseph. 2023. “Contact-Free Multitarget Tracking Using Distributed Massive MIMO-OFDM Communication System : Prototype and Analysis.” IEEE INTERNET OF THINGS JOURNAL 10 (10): 9220–33. https://doi.org/10.1109/JIOT.2023.3234041.
Chicago author-date (all authors)
Li, Chenglong, Sibren De Bast, Yang Miao, Emmeric Tanghe, Sofie Pollin, and Wout Joseph. 2023. “Contact-Free Multitarget Tracking Using Distributed Massive MIMO-OFDM Communication System : Prototype and Analysis.” IEEE INTERNET OF THINGS JOURNAL 10 (10): 9220–9233. doi:10.1109/JIOT.2023.3234041.
Vancouver
1.
Li C, De Bast S, Miao Y, Tanghe E, Pollin S, Joseph W. Contact-free multitarget tracking using distributed Massive MIMO-OFDM communication system : prototype and analysis. IEEE INTERNET OF THINGS JOURNAL. 2023;10(10):9220–33.
IEEE
[1]
C. Li, S. De Bast, Y. Miao, E. Tanghe, S. Pollin, and W. Joseph, “Contact-free multitarget tracking using distributed Massive MIMO-OFDM communication system : prototype and analysis,” IEEE INTERNET OF THINGS JOURNAL, vol. 10, no. 10, pp. 9220–9233, 2023.
@article{01HD113J4BDW8Z1RVE2NKFPZBC,
  abstract     = {{Wireless-based human activity recognition has become an essential technology that enables contact-free human-machine and human-environment interactions. In this article, we consider contact-free multitarget tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6-GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then, the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets' locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density (GM-PHD) filter for tracking. A multipedestrian tracking experiment is conducted in a room with a size of 6.5 mx10 m to evaluate the performance of the proposed algorithm. According to the experimental results, we achieve 75th and 95th percentile accuracy of 12.7 and 18.2 cm for single-person tracking and 28.9 and 45.7 cm for multiperson tracking, respectively. Furthermore, the proposed algorithm achieves tracking purposes in real time, which is promising for practical MTT use cases.}},
  author       = {{Li, Chenglong and De Bast, Sibren and Miao, Yang and Tanghe, Emmeric and Pollin, Sofie and Joseph, Wout}},
  issn         = {{2327-4662}},
  journal      = {{IEEE INTERNET OF THINGS JOURNAL}},
  keywords     = {{OPPORTUNITIES,LOCALIZATION,Channel state information (CSI),indoor localization,integrated sensing and communication (ISAC),massive multiple-input and multiple-output (MIMO),multitarget tracking (MTT),radar}},
  language     = {{eng}},
  number       = {{10}},
  pages        = {{9220--9233}},
  title        = {{Contact-free multitarget tracking using distributed Massive MIMO-OFDM communication system : prototype and analysis}},
  url          = {{http://doi.org/10.1109/JIOT.2023.3234041}},
  volume       = {{10}},
  year         = {{2023}},
}
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