Probabilistic Fingerprinting Based Passive Device-free Localization from Channel State Information

Probabilistic Fingerprinting Based Passive Device-free Localization from Channel State Information. Shi, S., Sigg, S., & Ji, Y. In Vehicular Technology Conference (vtc 2016-spring), May, 2016.
abstract bibtex

Given the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface, researchers have shown great interest in analyzing signal ?uctuation on this interface for environmental perception. A popular example is the enabling of indoor localization with RF signals. As an alternative to active device-based positioning, device-free passive (DfP) indoor localization has the advantage that the sensed individuals do not require to carry RF sensors. We propose a probabilistic ?ngerprinting-based technique for DfP indoor localization. Our system adopts CSI readings derived from off-the-shelf WiFi 802.11n wireless cards which can provide fine-grained subchannel measurements in the context of MIMOOFDM PHY layer parameters. This complex channel information enables accurate localization of non-equipped individualsOur scheme further boosts the localization efficiency by using principal component analysis (PCA) to identify the most relevanfeature vectors. The experimental results demonstrate that our system can achieve an accuracy of over 92% and an error distance smaller than 0:5m. We also investigate the effect of other parameters on the performance of our system, including packetransmission rate the number of links as well as the number of principle components.

@InProceedings{Shi_2016_vtc,
author={Shuyu Shi and Stephan Sigg and Yusheng Ji},
title={Probabilistic Fingerprinting Based Passive Device-free Localization from Channel State Information},
booktitle={Vehicular Technology Conference (vtc 2016-spring)},
year={2016},
month={May},
abstract={Given the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface, researchers
have shown great interest in analyzing signal ?uctuation on
this interface for environmental perception. A popular example
is the enabling of indoor localization with RF signals. As an
alternative to active device-based positioning, device-free passive
(DfP) indoor localization has the advantage that the sensed
individuals do not require to carry RF sensors.
We propose a probabilistic ?ngerprinting-based technique for
DfP indoor localization. Our system adopts CSI readings derived
from off-the-shelf WiFi 802.11n wireless cards which can provide fine-grained subchannel measurements in the context of MIMOOFDM PHY layer parameters. This complex channel information enables accurate localization of non-equipped individualsOur scheme further boosts the localization efficiency by using
principal component analysis (PCA) to identify the most relevanfeature vectors. The experimental results demonstrate that our
system can achieve an accuracy of over 92% and an error
distance smaller than 0:5m. We also investigate the effect of other
parameters on the performance of our system, including packetransmission rate the number of links as well as the number of principle components.
},
group = {ambience}}

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A popular example is the enabling of indoor localization with RF signals. As an alternative to active device-based positioning, device-free passive (DfP) indoor localization has the advantage that the sensed individuals do not require to carry RF sensors. We propose a probabilistic ?ngerprinting-based technique for DfP indoor localization. Our system adopts CSI readings derived from off-the-shelf WiFi 802.11n wireless cards which can provide fine-grained subchannel measurements in the context of MIMOOFDM PHY layer parameters. This complex channel information enables accurate localization of non-equipped individualsOur scheme further boosts the localization efficiency by using principal component analysis (PCA) to identify the most relevanfeature vectors. The experimental results demonstrate that our system can achieve an accuracy of over 92% and an error distance smaller than 0:5m. We also investigate the effect of other parameters on the performance of our system, including packetransmission rate the number of links as well as the number of principle components. ","group":"ambience","bibtex":"@InProceedings{Shi_2016_vtc,\nauthor={Shuyu Shi and Stephan Sigg and Yusheng Ji},\ntitle={Probabilistic Fingerprinting Based Passive Device-free Localization from Channel State Information},\nbooktitle={Vehicular Technology Conference (vtc 2016-spring)},\nyear={2016},\nmonth={May},\nabstract={Given the ubiquitous distribution of electronic devices equipped with a radio frequency (RF) interface, researchers\nhave shown great interest in analyzing signal ?uctuation on\nthis interface for environmental perception. A popular example\nis the enabling of indoor localization with RF signals. As an\nalternative to active device-based positioning, device-free passive\n(DfP) indoor localization has the advantage that the sensed\nindividuals do not require to carry RF sensors.\nWe propose a probabilistic ?ngerprinting-based technique for\nDfP indoor localization. Our system adopts CSI readings derived\nfrom off-the-shelf WiFi 802.11n wireless cards which can provide fine-grained subchannel measurements in the context of MIMOOFDM PHY layer parameters. This complex channel information enables accurate localization of non-equipped individualsOur scheme further boosts the localization efficiency by using\nprincipal component analysis (PCA) to identify the most relevanfeature vectors. The experimental results demonstrate that our\nsystem can achieve an accuracy of over 92% and an error\ndistance smaller than 0:5m. 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