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Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches

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Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches. / Yao, Lina; Ruan, Wenjie; Sheng, Quan Z. et al.

CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, 2014. p. 1799-1802.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Yao, L, Ruan, W, Sheng, QZ, Li, X & Falkner, NJG 2014, Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches. in CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, pp. 1799-1802, 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, 3/11/14. https://doi.org/10.1145/2661829.2661873

APA

Yao, L., Ruan, W., Sheng, Q. Z., Li, X., & Falkner, N. J. G. (2014). Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management (pp. 1799-1802). Association for Computing Machinery, Inc. https://doi.org/10.1145/2661829.2661873

Vancouver

Yao L, Ruan W, Sheng QZ, Li X, Falkner NJG. Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc. 2014. p. 1799-1802 doi: 10.1145/2661829.2661873

Author

Yao, Lina ; Ruan, Wenjie ; Sheng, Quan Z. et al. / Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches. CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, 2014. pp. 1799-1802

Bibtex

@inproceedings{c5ba1f77b4d04d2cafa54b9531ed08fb,
title = "Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches",
abstract = "RFID-based localization and tracking has some promising potentials. By combining localization with its identification capability, existing applications can be enhanced and new applications can be developed. In this paper, we investigate a tag-free indoor localizing and tracking problem (e.g., people tracking) without requiring subjects to carry any tags or devices in a pure passive environment. We formulate localization as a classification task. In particular, we model the received signal strength indicator (RSSI) of passive tags using multivariate Gaussian Mixture Model (GMM), and use the Expectation Maximization (EM) to learn the maximum likelihood estimates of the model parameters. Several other learning-based probabilistic approaches are also explored in the localization problem. To track a moving subject, we propose GMM based Hidden Markov Model (HMM) and k Nearest Neighbor (kNN) based HMM approaches. We conduct extensive experiments in a testbed formed by passive RFID tags, and the experimental results demonstrate the effectiveness and accuracy of our approach.",
keywords = "Gaussian mixture model, Hidden Markov model, Kernel-based, Localization, Nearest neighbour, RFID",
author = "Lina Yao and Wenjie Ruan and Sheng, {Quan Z.} and Xue Li and Falkner, {Nicholas J.G.}",
year = "2014",
month = jan,
day = "1",
doi = "10.1145/2661829.2661873",
language = "English",
pages = "1799--1802",
booktitle = "CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management",
publisher = "Association for Computing Machinery, Inc",
note = "23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 ; Conference date: 03-11-2014 Through 07-11-2014",

}

RIS

TY - GEN

T1 - Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches

AU - Yao, Lina

AU - Ruan, Wenjie

AU - Sheng, Quan Z.

AU - Li, Xue

AU - Falkner, Nicholas J.G.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - RFID-based localization and tracking has some promising potentials. By combining localization with its identification capability, existing applications can be enhanced and new applications can be developed. In this paper, we investigate a tag-free indoor localizing and tracking problem (e.g., people tracking) without requiring subjects to carry any tags or devices in a pure passive environment. We formulate localization as a classification task. In particular, we model the received signal strength indicator (RSSI) of passive tags using multivariate Gaussian Mixture Model (GMM), and use the Expectation Maximization (EM) to learn the maximum likelihood estimates of the model parameters. Several other learning-based probabilistic approaches are also explored in the localization problem. To track a moving subject, we propose GMM based Hidden Markov Model (HMM) and k Nearest Neighbor (kNN) based HMM approaches. We conduct extensive experiments in a testbed formed by passive RFID tags, and the experimental results demonstrate the effectiveness and accuracy of our approach.

AB - RFID-based localization and tracking has some promising potentials. By combining localization with its identification capability, existing applications can be enhanced and new applications can be developed. In this paper, we investigate a tag-free indoor localizing and tracking problem (e.g., people tracking) without requiring subjects to carry any tags or devices in a pure passive environment. We formulate localization as a classification task. In particular, we model the received signal strength indicator (RSSI) of passive tags using multivariate Gaussian Mixture Model (GMM), and use the Expectation Maximization (EM) to learn the maximum likelihood estimates of the model parameters. Several other learning-based probabilistic approaches are also explored in the localization problem. To track a moving subject, we propose GMM based Hidden Markov Model (HMM) and k Nearest Neighbor (kNN) based HMM approaches. We conduct extensive experiments in a testbed formed by passive RFID tags, and the experimental results demonstrate the effectiveness and accuracy of our approach.

KW - Gaussian mixture model

KW - Hidden Markov model

KW - Kernel-based

KW - Localization

KW - Nearest neighbour

KW - RFID

U2 - 10.1145/2661829.2661873

DO - 10.1145/2661829.2661873

M3 - Conference contribution/Paper

AN - SCOPUS:84937597354

SP - 1799

EP - 1802

BT - CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

PB - Association for Computing Machinery, Inc

T2 - 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014

Y2 - 3 November 2014 through 7 November 2014

ER -