Dual-Band Sensor Network for Accurate Device-Free Localization in Indoor Environment with WiFi Interference

Manyi WANG; Zhonglei WANG; Enjie DING; Yun YANG

doi:10.1587/transinf.2014NTP0007

Dual-Band Sensor Network for Accurate Device-Free Localization in Indoor Environment with WiFi Interference

Manyi WANG, Zhonglei WANG, Enjie DING, Yun YANG

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Radio Frequency based Device-Free Localization (RFDFL) is an emerging localization technique without requirements of attaching any electronic device to a target. The target can be localized by means of measuring the shadowing of received signal strength caused by the target. However, the accuracy of RFDFL deteriorates seriously in environment with WiFi interference. State-of-the-art methods do not efficiently solve this problem. In this paper, we propose a dual-band method to improve the accuracy of RFDFL in environment without/with severe WiFi interference. We introduce an algorithm of fusing dual-band images in order to obtain an enhanced image inferring more precise location and propose a timestamp-based synchronization method to associate the dual-band images to ensure their one-one correspondence. With real-world experiments, we show that our method outperforms traditional single-band localization methods and improves the localization accuracy by up to 40.4% in real indoor environment with high WiFi interference.

Publication: IEICE TRANSACTIONS on Information Vol.E98-D No.3 pp.596-606

Publication Date: 2015/03/01

Publicized: 2014/12/11

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2014NTP0007

Type of Manuscript: Special Section PAPER (Special Section on the Architectures, Protocols, and Applications for the Future Internet)

Category: Network Computing and Applications

Authors

Manyi WANG
  China University of Mining and Technology,Karlsruhe Institute of Technology (KIT)
Zhonglei WANG
  Karlsruhe Institute of Technology (KIT)
Enjie DING
  China University of Mining and Technology
Yun YANG
  CNRS/CEA/UJF/INAC,Osaka University

Keyword

device-free localization, WiFi interference, dual-band, sensor networks

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Manyi WANG, Zhonglei WANG, Enjie DING, Yun YANG, "Dual-Band Sensor Network for Accurate Device-Free Localization in Indoor Environment with WiFi Interference" in IEICE TRANSACTIONS on Information, vol. E98-D, no. 3, pp. 596-606, March 2015, doi: 10.1587/transinf.2014NTP0007.
Abstract: Radio Frequency based Device-Free Localization (RFDFL) is an emerging localization technique without requirements of attaching any electronic device to a target. The target can be localized by means of measuring the shadowing of received signal strength caused by the target. However, the accuracy of RFDFL deteriorates seriously in environment with WiFi interference. State-of-the-art methods do not efficiently solve this problem. In this paper, we propose a dual-band method to improve the accuracy of RFDFL in environment without/with severe WiFi interference. We introduce an algorithm of fusing dual-band images in order to obtain an enhanced image inferring more precise location and propose a timestamp-based synchronization method to associate the dual-band images to ensure their one-one correspondence. With real-world experiments, we show that our method outperforms traditional single-band localization methods and improves the localization accuracy by up to 40.4% in real indoor environment with high WiFi interference.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2014NTP0007/_p

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@ARTICLE{e98-d_3_596,
author={Manyi WANG, Zhonglei WANG, Enjie DING, Yun YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Dual-Band Sensor Network for Accurate Device-Free Localization in Indoor Environment with WiFi Interference},
year={2015},
volume={E98-D},
number={3},
pages={596-606},
abstract={Radio Frequency based Device-Free Localization (RFDFL) is an emerging localization technique without requirements of attaching any electronic device to a target. The target can be localized by means of measuring the shadowing of received signal strength caused by the target. However, the accuracy of RFDFL deteriorates seriously in environment with WiFi interference. State-of-the-art methods do not efficiently solve this problem. In this paper, we propose a dual-band method to improve the accuracy of RFDFL in environment without/with severe WiFi interference. We introduce an algorithm of fusing dual-band images in order to obtain an enhanced image inferring more precise location and propose a timestamp-based synchronization method to associate the dual-band images to ensure their one-one correspondence. With real-world experiments, we show that our method outperforms traditional single-band localization methods and improves the localization accuracy by up to 40.4% in real indoor environment with high WiFi interference.},
keywords={},
doi={10.1587/transinf.2014NTP0007},
ISSN={1745-1361},
month={March},}

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TY - JOUR
TI - Dual-Band Sensor Network for Accurate Device-Free Localization in Indoor Environment with WiFi Interference
T2 - IEICE TRANSACTIONS on Information
SP - 596
EP - 606
AU - Manyi WANG
AU - Zhonglei WANG
AU - Enjie DING
AU - Yun YANG
PY - 2015
DO - 10.1587/transinf.2014NTP0007
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2015
AB - Radio Frequency based Device-Free Localization (RFDFL) is an emerging localization technique without requirements of attaching any electronic device to a target. The target can be localized by means of measuring the shadowing of received signal strength caused by the target. However, the accuracy of RFDFL deteriorates seriously in environment with WiFi interference. State-of-the-art methods do not efficiently solve this problem. In this paper, we propose a dual-band method to improve the accuracy of RFDFL in environment without/with severe WiFi interference. We introduce an algorithm of fusing dual-band images in order to obtain an enhanced image inferring more precise location and propose a timestamp-based synchronization method to associate the dual-band images to ensure their one-one correspondence. With real-world experiments, we show that our method outperforms traditional single-band localization methods and improves the localization accuracy by up to 40.4% in real indoor environment with high WiFi interference.
ER -