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Indoor Localization via Discriminatively Regularized Least Square Classification

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Abstract

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.

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Authors and Affiliations

  1. Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Robin Wentao Ouyang, Albert Kai-Sun Wong & Kam Tim Woo

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  1. Robin Wentao Ouyang
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  2. Albert Kai-Sun Wong
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  3. Kam Tim Woo
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Correspondence to Robin Wentao Ouyang.

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Ouyang, R.W., Wong, A.KS. & Woo, K.T. Indoor Localization via Discriminatively Regularized Least Square Classification. Int J Wireless Inf Networks 18, 57–72 (2011). https://doi.org/10.1007/s10776-011-0133-5

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  • DOI: https://doi.org/10.1007/s10776-011-0133-5

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