Presentation 2014-01-23
A study on improvement of NLOS detection accuracy in TOA-based indoor localization
Manato HORIBA, Eiji OKAMOTO, Toshiko SHINOHARA, Katsuhiko MATSUMURA,
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Abstract(in English) Indoor localization scheme using sensor networks is expected to be applied in various fields, and the localization scheme using time of arrival (TOA) is well-known. However, the estimation accuracy of TOA localization is severely deteriorated in non-line-of-sight (NLOS) environments, and the deterioration is eased by applying iterative minimum residual (IMR) scheme. The IMR scheme is well applied with lower calculation complexity. However, when there are a lot of NLOS nodes, the NLOS detection errors increase in the IMR scheme and the estimation accuracy deteriorates. Therefore, in this paper, we propose a new scheme exploiting rough NLOS detection based on stochastic characteristics before the application of IMR scheme to improve the localization accuracy.
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Keyword(in English) sensor network / TOA position estimation / NLOS environment / iterative minimum residual scheme
Paper # ASN2013-123
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Committee ASN
Conference Date 2014/1/16(1days)
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Registration To Ambient intelligence and Sensor Networks(ASN)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A study on improvement of NLOS detection accuracy in TOA-based indoor localization
Sub Title (in English)
Keyword(1) sensor network
Keyword(2) TOA position estimation
Keyword(3) NLOS environment
Keyword(4) iterative minimum residual scheme
1st Author's Name Manato HORIBA
1st Author's Affiliation Department of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology()
2nd Author's Name Eiji OKAMOTO
2nd Author's Affiliation Department of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology
3rd Author's Name Toshiko SHINOHARA
3rd Author's Affiliation Daifuku Co., Ltd.
4th Author's Name Katsuhiko MATSUMURA
4th Author's Affiliation Daifuku Co., Ltd.
Date 2014-01-23
Paper # ASN2013-123
Volume (vol) vol.113
Number (no) 399
Page pp.pp.-
#Pages 6
Date of Issue