LiFS: Low human-effort, device-free localization with fine-grained subcarrier information

Ju Wang, Hongbo Jiang, Jie Xiong, Kyle Jamieson, Xiaojiang Chen, Dingyi Fang, Binbin Xie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

108 Scopus citations

Abstract

Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate modelbased device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization. We design, implement and evaluate LiFS with extensive experiments in three different environments. Without 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. Besides single target localization, LiFS is able to differentiate two sparsely-located targets and localize each of them at a high accuracy.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
PublisherAssociation for Computing Machinery
Pages243-256
Number of pages14
Edition1
ISBN (Print)9781450342261
DOIs
StatePublished - Oct 3 2016
Event22nd Annual International Conference on Mobile Computing and Networking, MobiCom 2016 - New York, United States
Duration: Oct 3 2016Oct 7 2016

Publication series

NameProceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
Number1
Volume0

Other

Other22nd Annual International Conference on Mobile Computing and Networking, MobiCom 2016
CountryUnited States
CityNew York
Period10/3/1610/7/16

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

Keywords

  • Channel state information
  • Device-free localization
  • Low human-effort
  • Multipath
  • Power fading model

Fingerprint Dive into the research topics of 'LiFS: Low human-effort, device-free localization with fine-grained subcarrier information'. Together they form a unique fingerprint.

  • Cite this

    Wang, J., Jiang, H., Xiong, J., Jamieson, K., Chen, X., Fang, D., & Xie, B. (2016). LiFS: Low human-effort, device-free localization with fine-grained subcarrier information. In Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM (1 ed., pp. 243-256). (Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM; Vol. 0, No. 1). Association for Computing Machinery. https://doi.org/10.1145/2973750.2973776