The Gray Analysis and Machine Learning for Device-Free Multitarget Localization in Passive UHF RFID Environments

Ma, Yongtao; Wang, Bobo; Gao, Xin; Ning, Wanru

doi:10.1109/tii.2019.2921529

Cited by 45 publications

(11 citation statements)

References 37 publications

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“…16(d), displaying an error less than 18 cm with DS and 21 cm with Capon, over all experiments. This proposed technology-with only eight tags-outperforms previously proposed UHF-based localization techniques that require at least 20 to 30 tags to enable (x, y) localization [19]- [21]. An example of the results obtained from the conducted experiments is shown in Fig.…”

Section: B Scenario Of Eight Orthogonal Tags Arraymentioning

confidence: 86%

“…Ma et al [19] demonstrated target localization with 0.24-m median error using an impractically large two TX/RX reader-antennas system and a set of 50 tags placed on two orthogonal walls. Unlike [19] that takes advantage of both received signal strength indicator (RSSI) and phase information, [20] and [21] rely solely on the RSSI information to accurately localize targets in indoor environments. Their hardware consists of four reader antennas and 30 RFID tags.…”

Section: Related Workmentioning

confidence: 99%

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Holography-Based Target Localization and Health Monitoring Technique Using UHF Tags Array

Eid

Zhu

et al. 2021

IEEE Internet Things J.

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Radio technologies are appealing for unobtrusive and remote monitoring of human activities. Radar-based human activity recognition proves to be a success, for example, Project Soli developed by Google. However, it is expensive to scale up for multiuser environments. In this article, we propose a solutionthe HoloTag system-which circumvents the multichannel-radar scaling problem through the use of a quasivirtual ultralow-cost UHF RFID array over which a holographic projection of its environment is measured and used to both localize and monitor the health of several targets. The method is first described in detail, before the image reconstruction process, employing known beamforming algorithms-Delay & Sum, and Capon-is shown and its scaling properties simulated. Then, the idiosyncrasies of the implementation of HoloTag using low-cost off-the-shelf hardware are explained, before its ability to simultaneously measure the breathing rates and positions of multiple real and synthetic targets with accuracies of better than 0.8 bpm and 20 cm is demonstrated.

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Section: B Scenario Of Eight Orthogonal Tags Arraymentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Holography-Based Target Localization and Health Monitoring Technique Using UHF Tags Array

Eid

Zhu

et al. 2021

IEEE Internet Things J.

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“…In [32] the authors propose a system with a moving reader antenna measuring the multipath profiles of the reference tags to enhance tag localization accuracy, according to the insight that nearby tags have similar multipath effects. The use of reference tags was also combined with machine-learning techniques such as the Support Vector Regression [33], Artificial Neural Networks [34] and the Naïve Bayes algorithm [35].…”

Section: Related Workmentioning

confidence: 99%

Robot-Based Indoor Positioning of UHF-RFID Tags: The SAR Method With Multiple Trajectories

Bernardini

Buffi

Fontanelli

et al. 2021

IEEE Trans. Instrum. Meas.

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This paper presents the application of the Synthetic Aperture Radar (SAR) localization method for indoor positioning of UHF-RFID tags when the robot-mounted reader antenna moves alongside multiple trajectories. By properly combining the phase data associated to a set of multiple paths, the synthetic apertures along the main directions enlarge and then the localization accuracy may improve. Besides, during consecutive inventory rounds, several tag position estimates are available and they can be profitably combined to minimize the localization uncertainty. Different combination approaches are investigated to determine the best choice to improve the localization performance. The method capabilities are discussed through a numerical analysis, by considering different configurations of the multiple apertures and different sources of measurement uncertainty. Finally, the proposed localization method is validated through an experimental analysis carried out with commercial RFID hardware and a robotic wheeled walker, in an indoor scenario, by employing different types of tags. The knowledge of the reader/robot trajectory required by the SAR method is here achieved with an optical system.

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“…Although it is more common in scene analysis for the existence of a device on the pedestrian to be localized, some researchers, such as in [43], [50], [68], [71], [75], [78], [79], are interested in device-free localization. An interesting proposal for device-free localization is presented in [75], in which the authors use RF to classify the error caused by environment changes and correct the fingerprint errors.…”

Section: A Machine Learning In Scene Analysismentioning

confidence: 99%

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

et al. 2021

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With the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.

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The Gray Analysis and Machine Learning for Device-Free Multitarget Localization in Passive UHF RFID Environments

Cited by 45 publications

References 37 publications

Holography-Based Target Localization and Health Monitoring Technique Using UHF Tags Array

Holography-Based Target Localization and Health Monitoring Technique Using UHF Tags Array

Robot-Based Indoor Positioning of UHF-RFID Tags: The SAR Method With Multiple Trajectories

A Survey of Machine Learning in Pedestrian Localization Systems: Applications, Open Issues and Challenges

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