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Title:
device-clustering algorithm in crowdsourcing-based localization
Author: Cheng Huang ; Luo Hai-Yong ; Zhao Fang
Keyword: Navigation systems
Source: Journal of China Universities of Posts and Telecommunications
Issued Date: 2012
Volume: 19, Issue:SUPPL. 2, Pages:114-121
Indexed Type: EI
Department: (1) Software School Beijing University of Posts and Telecommunications Beijing 100876 China; (2) Institute of Computing Technology Chinese Academy of Sciences Beijing 100876 China
Abstract: Device heterogeneity significantly degrades the localization performance of fingerprinting-based localization, especially in the crowdsourcing-based positioning system. Although manual calibration can reduce positional error, the adjustment overhead is extremely heavy and to maintain ever-increasing device types is overly laborious. In this paper, we propose a novel device-clustering algorithm to operate the positioning system based on macro device-cluster (DC) rather than natural device. In this way, the system maintains less device types and the localization accuracy is improved obviously. The experimental result of different combination indicates the optimal operating flow is to combine DC and kernel density estimator when the tracking device is known and add the linear transformation phase when device is unknown. © 2012 The Journal of China Universities of Posts and Telecommunications.
English Abstract: Device heterogeneity significantly degrades the localization performance of fingerprinting-based localization, especially in the crowdsourcing-based positioning system. Although manual calibration can reduce positional error, the adjustment overhead is extremely heavy and to maintain ever-increasing device types is overly laborious. In this paper, we propose a novel device-clustering algorithm to operate the positioning system based on macro device-cluster (DC) rather than natural device. In this way, the system maintains less device types and the localization accuracy is improved obviously. The experimental result of different combination indicates the optimal operating flow is to combine DC and kernel density estimator when the tracking device is known and add the linear transformation phase when device is unknown. © 2012 The Journal of China Universities of Posts and Telecommunications.
Language: 英语
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/15442
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Cheng Huang,Luo Hai-Yong,Zhao Fang. device-clustering algorithm in crowdsourcing-based localization[J]. Journal of China Universities of Posts and Telecommunications,2012-01-01,19(SUPPL. 2):114-121.
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