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

Lina Yao, Wenjie Ruan, Quan Z. Sheng, Xue Li, Nicholas J.G. Falkner

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

12 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationCIKM 2014
Subtitle of host publicationProceedings of the 2014 ACM International Conference on Information and Knowledge Management
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages1799-1802
Number of pages4
ISBN (Electronic)9781450325981
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: 3 Nov 20147 Nov 2014

Other

Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
CountryChina
CityShanghai
Period3/11/147/11/14

Keywords

  • localization
  • RFID
  • Hidden Markov Model
  • Gaussian Mixture Model
  • kernel-based
  • nearest neighbour

Fingerprint Dive into the research topics of 'Exploring tag-free RFID-based passive localization and tracking via learning-based probabilistic approaches'. Together they form a unique fingerprint.

  • Cite this

    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). New York: Association for Computing Machinery, Inc. https://doi.org/10.1145/2661829.2661873