Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/40092

Indoor localization via discriminatively regularized least square classification

Authors Ouyang,wentao,Robin HKUST affiliated (currently or previously)
Wong,Kai Sun,Albert View this author's profile
Woo, Kam Tim View this author's profile
Issue Date 2011
Source International journal of wireless information networks , v. 18, (2), 2011, p. 57-72
Summary In this paper, we address the received signal strength (RSS)-based indoor localization problem in a wireless local area network (WLAN) environment and formulate it as a multi-class classification problem using survey locations as classes. We present a discriminatively regularized least square classifier (DRLSC)-based localization algorithm that is aimed at making use of the class label information to better distinguish the RSS samples taken from different locations after proper transformation. Besides DRLSC, two other regularized least square classifiers (RLSCs) are also presented for comparison. We show that these RLSCs can be expressed in a unified problem formulation with a closed-form solution and convenient assessment of the convexity of the problem. We then extend the linear RLSCs to their nonlinear counterparts via the kernel trick. Moreover, we address the missing value problem, utilize clustering to reduce the training and online complexity, and introduce kernel alignment for fast kernel parameter tuning. Experimental results show that, compared with other methods, the kernel DRLSC-based algorithm achieves superior performance for indoor localization when only a small fraction of the data samples are used. © Springer Science+Business Media, LLC 2011.
Subjects
ISSN 1068-9605
Language English
Format Article
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