NDLI: A statistical indoor localization method for supporting location-based access control

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A statistical indoor localization method for supporting location-based access control

Content Provider	ACM Digital Library
Author	Yu, Zhen Wei, Yawen Gao, Chunwang Russell, Steve Guan, Yong
Abstract	Location awareness is critical for supporting location-based access control (LBAC). The challenge is how to determine locations accurately and efficiently in indoor environments. Existing solutions based on WLAN signal strength either cannot provide high accuracy, or are too complicated in general indoor environments. In this paper, we propose a statistical indoor localization method for supporting location-based access control. In an offline phase, we fit a LOESS [3, 4, 16] local regression model on a training set to build a radio map containing the distribution of signal strength. In an online phase, we estimate locations using Maximum Likelihood Estimation (MLE) [7, 8, 9] based on the measured signal strength and the stored distribution. A Bootstrapping method [11] is further exploited to give a confidence interval of estimation. Compared with others, our method is simpler, more systematic and more accurate. Experimental results show that the average error of our method is less than 2m. Hence, it can better support LBAC applications.
Starting Page	1
Ending Page	7
Page Count	7
File Format	PDF
ISBN	9789639799264
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-07-28
Publisher Place	Brussels
Access Restriction	Subscribed
Subject Keyword	Statistical indoor localization Wireless signal strength Location-based access control
Content Type	Text
Resource Type	Article

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