Accurate Location Tracking from CSI-based Passive Device-free Probabilistic Fingerprinting

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Accurate Location Tracking from CSI-based Passive Device-free Probabilistic Fingerprinting. / Shi, Shuyu; Sigg, Stephan; Chen, Lin; Ji, Yusheng.

In: IEEE Transactions on Vehicular Technology, Vol. 67, No. 6, 06.2018, p. 5217-5230.

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@article{0bbdb39bfb7a468587c16e8ce8c21773,
title = "Accurate Location Tracking from CSI-based Passive Device-free Probabilistic Fingerprinting",
abstract = "The research on indoor localization has received great interest in recent years. This has been fuelled by the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface. Analyzing the signal fluctuation on the RF-interface can, for instance, solve the still open issue of ubiquitous reliable indoor localization and tracking. Device bound and device free approaches with remarkable accuracy have been reported recently. In this paper, we present an accurate device-free passive (DfP) indoor location tracking system which adopts Channel- state Information (CSI) readings from off-the-shelf WiFi 802.11n wireless cards. The fine-grained subchannel measurements for MIMO-OFDM PHY layer parameters are exploited to improve localization and tracking accuracy. To enable precise positioning in the presence of heavy multipath effects in cluttered indoor scenarios, we experimentally validate the unpredictability of CSI measurements and suggest a probabilistic fingerprint-based technique as an accurate solution. Our scheme further boosts the localization efficiency by using principal component analysis (PCA) to filter the most relevant feature vectors. Furthermore, with Bayesian filtering, we continuously track the trajectory of a moving subject. We have evaluated the performance of our system in four indoor environments and compared it with state-of-art indoor localization schemes. Our experimental results demonstrate that this complex channel information enables more accurate localization of non-equipped individuals.",
author = "Shuyu Shi and Stephan Sigg and Lin Chen and Yusheng Ji",
year = "2018",
month = "6",
doi = "10.1109/TVT.2018.2810307",
language = "English",
volume = "67",
pages = "5217--5230",
journal = "IEEE Transactions on Vehicular Technology",
issn = "0018-9545",
number = "6",

}

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TY - JOUR

T1 - Accurate Location Tracking from CSI-based Passive Device-free Probabilistic Fingerprinting

AU - Shi, Shuyu

AU - Sigg, Stephan

AU - Chen, Lin

AU - Ji, Yusheng

PY - 2018/6

Y1 - 2018/6

N2 - The research on indoor localization has received great interest in recent years. This has been fuelled by the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface. Analyzing the signal fluctuation on the RF-interface can, for instance, solve the still open issue of ubiquitous reliable indoor localization and tracking. Device bound and device free approaches with remarkable accuracy have been reported recently. In this paper, we present an accurate device-free passive (DfP) indoor location tracking system which adopts Channel- state Information (CSI) readings from off-the-shelf WiFi 802.11n wireless cards. The fine-grained subchannel measurements for MIMO-OFDM PHY layer parameters are exploited to improve localization and tracking accuracy. To enable precise positioning in the presence of heavy multipath effects in cluttered indoor scenarios, we experimentally validate the unpredictability of CSI measurements and suggest a probabilistic fingerprint-based technique as an accurate solution. Our scheme further boosts the localization efficiency by using principal component analysis (PCA) to filter the most relevant feature vectors. Furthermore, with Bayesian filtering, we continuously track the trajectory of a moving subject. We have evaluated the performance of our system in four indoor environments and compared it with state-of-art indoor localization schemes. Our experimental results demonstrate that this complex channel information enables more accurate localization of non-equipped individuals.

AB - The research on indoor localization has received great interest in recent years. This has been fuelled by the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface. Analyzing the signal fluctuation on the RF-interface can, for instance, solve the still open issue of ubiquitous reliable indoor localization and tracking. Device bound and device free approaches with remarkable accuracy have been reported recently. In this paper, we present an accurate device-free passive (DfP) indoor location tracking system which adopts Channel- state Information (CSI) readings from off-the-shelf WiFi 802.11n wireless cards. The fine-grained subchannel measurements for MIMO-OFDM PHY layer parameters are exploited to improve localization and tracking accuracy. To enable precise positioning in the presence of heavy multipath effects in cluttered indoor scenarios, we experimentally validate the unpredictability of CSI measurements and suggest a probabilistic fingerprint-based technique as an accurate solution. Our scheme further boosts the localization efficiency by using principal component analysis (PCA) to filter the most relevant feature vectors. Furthermore, with Bayesian filtering, we continuously track the trajectory of a moving subject. We have evaluated the performance of our system in four indoor environments and compared it with state-of-art indoor localization schemes. Our experimental results demonstrate that this complex channel information enables more accurate localization of non-equipped individuals.

U2 - 10.1109/TVT.2018.2810307

DO - 10.1109/TVT.2018.2810307

M3 - Article

VL - 67

SP - 5217

EP - 5230

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

SN - 0018-9545

IS - 6

ER -

ID: 15343269