Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/95267
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Type: Conference paper
Title: TagTrack: device-free localization and tracking using passive RFID tags
Author: Ruan, W.
Yao, L.
Sheng, Q.
Falkner, N.
Li, X.
Citation: MobiQuitous 2014 - 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2014 / pp.80-89
Publisher: ICST
Issue Date: 2014
ISBN: 9781631900396
Conference Name: 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (02 Dec 2014 - 05 Dec 2014 : London, Great Britain)
Statement of
Responsibility: 
Wenjie Ruan, Lina Yao, Quan Z. Sheng, Nickolas Falkner, Xue Li
Abstract: Device-free passive localization aims to localize or track targets without requiring them to carry any devices or to be actively involved with the localization process. This technique has received much attention recently in a wide range of applications including elderly people surveillance, intruder detection, and indoor navigation. In this paper, we propose a novel localization and tracking system based on the Received Signal Strength field formed by a set of cost-efficient passive RFID tags. We firstly formulate localization as a classification task, where we compare several state-of-the-art learning-based classification methods including k Nearest Neighbor (kNN), Multivariate Gaussian Mixture Model (GMM) and Support Vector Machine (SVM). To track a moving subject, we propose two Hidden Markov Model (HMM)-based methods, namely GMM-based HMM and kNN-based HMM. kNN-based HMM extends kNN into a probabilistic style to approximate the Emission Probability Matrix in HMM. The proposed methods can be easily applied into other fingerprint-based tracking systems regardless of their hardware platforms. We conduct extensive experiments and the results demonstrate the effectiveness and accuracy of our approaches with up to 98% localization accuracy and an average of 0.7m tracking error.
Keywords: localization; rfid; hidden markov model; gaussian mix- ture model; kernel-based; nearest neighbor
Rights: Copyright © 2014–2015 ICST
RMID: 0030024770
DOI: 10.4108/icst.mobiquitous.2014.258004
Appears in Collections:Computer Science publications

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