TY - JOUR
T1 - Low Human-Effort, Device-Free Localization with Fine-Grained Subcarrier Information
AU - Wang, Ju
AU - Xiong, Jie
AU - Jiang, Hongbo
AU - Jamieson, Kyle
AU - Chen, Xiaojiang
AU - Fang, Dingyi
AU - Wang, Chen
N1 - Funding Information:
This work is supported in part by the National Natural Science Foundation of China under Grants (61572402, 61732017, 61672428, 61772422, 61572219), and by the National Science Foundation under Grant No. 1617161.
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2018/11/1
Y1 - 2018/11/1
N2 - Device-free localization of objects not equipped with RF radios is playing a critical role in many applications. This paper presents LIFS, a Low human-effort, device-free localization system with fine-grained subcarrier information, which can localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and thus the target can be localized by modelling the CSI measurements of multiple wireless links. However, due to rich multipath indoors, CSI can not be easily modelled. To deal with this challenge, our key observation is that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our CSI pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSI on the 'clean' subcarriers can still be utilized for accurate localization. Without the need of knowing the majority transceivers' locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively, outperforming the state-of-the-art systems.
AB - Device-free localization of objects not equipped with RF radios is playing a critical role in many applications. This paper presents LIFS, a Low human-effort, device-free localization system with fine-grained subcarrier information, which can localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and thus the target can be localized by modelling the CSI measurements of multiple wireless links. However, due to rich multipath indoors, CSI can not be easily modelled. To deal with this challenge, our key observation is that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our CSI pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSI on the 'clean' subcarriers can still be utilized for accurate localization. Without the need of knowing the majority transceivers' locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively, outperforming the state-of-the-art systems.
KW - Device-free localization
KW - channel state information
KW - low human-effort
KW - multipath
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U2 - 10.1109/TMC.2018.2812746
DO - 10.1109/TMC.2018.2812746
M3 - Article
AN - SCOPUS:85043456497
SN - 1536-1233
VL - 17
SP - 2550
EP - 2563
JO - IEEE Transactions on Mobile Computing
JF - IEEE Transactions on Mobile Computing
IS - 11
M1 - 8314084
ER -