Rapid Deployment Indoor Localization without Prior Human Participation

Han Xu; Zimu Zhou; Longfei Shangguan

doi:10.1109/LCN.2016.89

Rapid Deployment Indoor Localization without Prior Human Participation

Han Xu, Zimu Zhou, Longfei Shangguan

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator, fine-grained localization can be achieved by a discretized particle filter utilizing sensor data fusion. We devise techniques for CIVD-based field division, graph-based particle filter, EM-based individual character learning, and build a prototype that runs on commodity devices. Extensive experiments show that RAD provides a comparable performance to the state-of-the-art RSS-based methods while relieving it of prior human participation. © 2016 IEEE.

Original language	English
Title of host publication	Proceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016
Publisher	IEEE Computer Society
Pages	547-550
ISBN (Electronic)	9781509020546
ISBN (Print)	9781509020553
DOIs	https://doi.org/10.1109/LCN.2016.89
Publication status	Published - 2016
Externally published	Yes
Event	41st IEEE Conference on Local Computer Networks (LCN 2016) - Dubai, United Arab Emirates Duration: 7 Nov 2016 → 10 Nov 2016 https://www.ieeelcn.org/prior/LCN41/index.html

Publication series

Name	Proceedings - Conference on Local Computer Networks, LCN

Conference

Conference	41st IEEE Conference on Local Computer Networks (LCN 2016)
Abbreviated title	IEEE LCN 2016
Place	United Arab Emirates
City	Dubai
Period	7/11/16 → 10/11/16
Internet address	https://www.ieeelcn.org/prior/LCN41/index.html

Research Keywords

Field Division
Localization
Smart Phone

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10.1109/LCN.2016.89

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@inproceedings{65e04105456c46c585abd197a2a3e6bc,

title = "Rapid Deployment Indoor Localization without Prior Human Participation",

abstract = "In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator, fine-grained localization can be achieved by a discretized particle filter utilizing sensor data fusion. We devise techniques for CIVD-based field division, graph-based particle filter, EM-based individual character learning, and build a prototype that runs on commodity devices. Extensive experiments show that RAD provides a comparable performance to the state-of-the-art RSS-based methods while relieving it of prior human participation. {\textcopyright} 2016 IEEE.",

keywords = "Field Division, Localization, Smart Phone",

author = "Han Xu and Zimu Zhou and Longfei Shangguan",

year = "2016",

doi = "10.1109/LCN.2016.89",

language = "English",

isbn = "9781509020553",

series = "Proceedings - Conference on Local Computer Networks, LCN",

publisher = "IEEE Computer Society",

pages = "547--550",

booktitle = "Proceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016",

address = "United States",

note = "41st IEEE Conference on Local Computer Networks (LCN 2016), IEEE LCN 2016 ; Conference date: 07-11-2016 Through 10-11-2016",

url = "https://www.ieeelcn.org/prior/LCN41/index.html",

}

Xu, H, Zhou, Z & Shangguan, L 2016, Rapid Deployment Indoor Localization without Prior Human Participation. in Proceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016., 7796837, Proceedings - Conference on Local Computer Networks, LCN, IEEE Computer Society, pp. 547-550, 41st IEEE Conference on Local Computer Networks (LCN 2016), Dubai, United Arab Emirates, 7/11/16. https://doi.org/10.1109/LCN.2016.89

Rapid Deployment Indoor Localization without Prior Human Participation. / Xu, Han; Zhou, Zimu; Shangguan, Longfei.
Proceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016. IEEE Computer Society, 2016. p. 547-550 7796837 (Proceedings - Conference on Local Computer Networks, LCN).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Rapid Deployment Indoor Localization without Prior Human Participation

AU - Xu, Han

AU - Zhou, Zimu

AU - Shangguan, Longfei

PY - 2016

Y1 - 2016

N2 - In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator, fine-grained localization can be achieved by a discretized particle filter utilizing sensor data fusion. We devise techniques for CIVD-based field division, graph-based particle filter, EM-based individual character learning, and build a prototype that runs on commodity devices. Extensive experiments show that RAD provides a comparable performance to the state-of-the-art RSS-based methods while relieving it of prior human participation. © 2016 IEEE.

AB - In this work, we propose RAD, a RApid Deployment localization framework without human sampling. The basic idea of RAD is to automatically generate a fingerprint database through space partition, of which each cell is fingerprinted by its maximum influence APs. Based on this robust location indicator, fine-grained localization can be achieved by a discretized particle filter utilizing sensor data fusion. We devise techniques for CIVD-based field division, graph-based particle filter, EM-based individual character learning, and build a prototype that runs on commodity devices. Extensive experiments show that RAD provides a comparable performance to the state-of-the-art RSS-based methods while relieving it of prior human participation. © 2016 IEEE.

KW - Field Division

KW - Localization

KW - Smart Phone

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DO - 10.1109/LCN.2016.89

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509020553

T3 - Proceedings - Conference on Local Computer Networks, LCN

SP - 547

EP - 550

BT - Proceedings - 2016 IEEE 41st Conference on Local Computer Networks, LCN 2016

PB - IEEE Computer Society

T2 - 41st IEEE Conference on Local Computer Networks (LCN 2016)

Y2 - 7 November 2016 through 10 November 2016

ER -

Rapid Deployment Indoor Localization without Prior Human Participation

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Research Keywords

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