TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Efficient Kernel Based 3-D Source Localization via Tensor Completion |
| 영문제목(English Title) |
Efficient Kernel Based 3-D Source Localization via Tensor Completion |
| 저자(Author) |
Shan Lu
Jun Zhang
Xianmin Ma
Changju Kan
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| 원문수록처(Citation) |
VOL 13 NO. 01 PP. 0206 ~ 0221 (2019. 01) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Source localization in three-dimensional (3-D) wireless sensor networks (WSNs) is becoming a major research focus. Due to the complicated air-ground environments in 3-D positioning, many of the traditional localization methods, such as received signal strength (RSS) may have relatively poor accuracy performance. Benefit from prior learning mechanisms, fingerprinting-based localization methods are less sensitive to complex conditions and can provide relatively accurate localization performance. However, fingerprinting-based methods require training data at each grid point for constructing the fingerprint database, the overhead of which is very high, particularly for 3-D localization. Also, some of measured data may be unavailable due to the interference of a complicated environment. In this paper, we propose an efficient kernel based 3-D localization algorithm via tensor completion. We first exploit the spatial correlation of the RSS data and demonstrate the low rank property of the
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| 키워드(Keyword) |
Efficient source localization
received signal strength
spartial correlation
tensor completion
kernel learning
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| 파일첨부 |
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