A Dynamic HMM-Based Real-Time Location Tracking System Utilizing UHF Passive RFID

Bijoy Dripta Barua Chowdhury; Sara Masoud; Young Jun Son; Chieri Kubota; Russell Tronstad

doi:10.1109/JRFID.2021.3102507

A Dynamic HMM-Based Real-Time Location Tracking System Utilizing UHF Passive RFID

Bijoy Dripta Barua Chowdhury, Sara Masoud, Young Jun Son, Chieri Kubota, Russell Tronstad

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

In this paper, an indoor real-time location tracking system is proposed using ultra-high frequency passive radio frequency identification technology. A dynamic k nearest neighbor driven hidden Markov model is developed to track subsequent positions of moving objects by combining static localization with dynamic tracking. Statistically validated regression models are developed to relate the tags& x2019; received signal strength indicator and distance, which are iteratively updated over time to accommodate environmental changes. These models are utilized to generate a fingerprint database, which is used by the dynamic k nearest neighbor to estimate the set of potential locations for targeted tagged objects. The probabilistic likelihoods of tags& x2019; potential locations based on the current location and the received signal strength indicator(s) are used to construct the hidden Markov model& x2019;s transition and emission matrixes, respectively. Finally, the performance of the proposed framework is evaluated by a set of experiments given different factors, such as movement pattern and speed. Results reveal that our proposed framework can track objects with an average tracking error as low as 0.36m.

Original language	English (US)
Pages (from-to)	41-53
Number of pages	13
Journal	IEEE Journal of Radio Frequency Identification
Volume	6
DOIs	https://doi.org/10.1109/JRFID.2021.3102507
State	Published - 2022

Keywords

Estimation
Fingerprint recognition
Hidden Markov models
Location awareness
Passive RFID tags
Radiofrequency identification
Vehicle dynamics

ASJC Scopus subject areas

Instrumentation
Computer Networks and Communications

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10.1109/JRFID.2021.3102507

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@article{fda54b26efdf4633829d4b1358e01ab2,

title = "A Dynamic HMM-Based Real-Time Location Tracking System Utilizing UHF Passive RFID",

abstract = "In this paper, an indoor real-time location tracking system is proposed using ultra-high frequency passive radio frequency identification technology. A dynamic k nearest neighbor driven hidden Markov model is developed to track subsequent positions of moving objects by combining static localization with dynamic tracking. Statistically validated regression models are developed to relate the tags& x2019; received signal strength indicator and distance, which are iteratively updated over time to accommodate environmental changes. These models are utilized to generate a fingerprint database, which is used by the dynamic k nearest neighbor to estimate the set of potential locations for targeted tagged objects. The probabilistic likelihoods of tags& x2019; potential locations based on the current location and the received signal strength indicator(s) are used to construct the hidden Markov model& x2019;s transition and emission matrixes, respectively. Finally, the performance of the proposed framework is evaluated by a set of experiments given different factors, such as movement pattern and speed. Results reveal that our proposed framework can track objects with an average tracking error as low as 0.36m.",

keywords = "Estimation, Fingerprint recognition, Hidden Markov models, Location awareness, Passive RFID tags, Radiofrequency identification, Vehicle dynamics",

author = "Chowdhury, {Bijoy Dripta Barua} and Sara Masoud and Son, {Young Jun} and Chieri Kubota and Russell Tronstad",

note = "Publisher Copyright: 2469-7281 {\textcopyright} 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.",

year = "2022",

doi = "10.1109/JRFID.2021.3102507",

language = "English (US)",

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AU - Masoud, Sara

AU - Son, Young Jun

AU - Kubota, Chieri

AU - Tronstad, Russell

N1 - Publisher Copyright: 2469-7281 © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

PY - 2022

Y1 - 2022

N2 - In this paper, an indoor real-time location tracking system is proposed using ultra-high frequency passive radio frequency identification technology. A dynamic k nearest neighbor driven hidden Markov model is developed to track subsequent positions of moving objects by combining static localization with dynamic tracking. Statistically validated regression models are developed to relate the tags& x2019; received signal strength indicator and distance, which are iteratively updated over time to accommodate environmental changes. These models are utilized to generate a fingerprint database, which is used by the dynamic k nearest neighbor to estimate the set of potential locations for targeted tagged objects. The probabilistic likelihoods of tags& x2019; potential locations based on the current location and the received signal strength indicator(s) are used to construct the hidden Markov model& x2019;s transition and emission matrixes, respectively. Finally, the performance of the proposed framework is evaluated by a set of experiments given different factors, such as movement pattern and speed. Results reveal that our proposed framework can track objects with an average tracking error as low as 0.36m.

AB - In this paper, an indoor real-time location tracking system is proposed using ultra-high frequency passive radio frequency identification technology. A dynamic k nearest neighbor driven hidden Markov model is developed to track subsequent positions of moving objects by combining static localization with dynamic tracking. Statistically validated regression models are developed to relate the tags& x2019; received signal strength indicator and distance, which are iteratively updated over time to accommodate environmental changes. These models are utilized to generate a fingerprint database, which is used by the dynamic k nearest neighbor to estimate the set of potential locations for targeted tagged objects. The probabilistic likelihoods of tags& x2019; potential locations based on the current location and the received signal strength indicator(s) are used to construct the hidden Markov model& x2019;s transition and emission matrixes, respectively. Finally, the performance of the proposed framework is evaluated by a set of experiments given different factors, such as movement pattern and speed. Results reveal that our proposed framework can track objects with an average tracking error as low as 0.36m.

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KW - Hidden Markov models

KW - Location awareness

KW - Passive RFID tags

KW - Radiofrequency identification

KW - Vehicle dynamics

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A Dynamic HMM-Based Real-Time Location Tracking System Utilizing UHF Passive RFID

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Keywords

ASJC Scopus subject areas

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