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WLAN Indoor Positioning Algorithm Based on KDDA and SVR |
Xu Yu-bin Deng Zhi-an Ma Lin |
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China |
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Abstract The time-varying Received Signal Strength (RSS) drastically degrades the indoor positioning accuracy in Wireless Local Area Network (WLAN). A new positioning algorithm based on Kernel Direct Discriminant Analysis (KDDA) and Support Vector Regression (SVR) is proposed to resolve the problem in this paper. The proposed algorithm employs KDDA to reconstruct the localization information contained in the RSS signal. The most discriminative localization features are then extracted while the redundant localization features and noise are discarded by KDDA. The extracted localization features are taken as inputs to SVR learning machine and the mapping between localization features and physical locations is established. The experimental results show that the proposed algorithm obtains significant accuracy improvement while requiring a much smaller set of RSS training data than previous methods.
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Received: 05 August 2010
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Corresponding Authors:
Deng Zhi-an
E-mail: dengzhianan@163.com
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