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Error Estimation Method for Matrix Correlation-Based Wi-Fi Indoor Localization
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  • Error Estimation Method for Matrix Correlation-Based Wi-Fi Indoor Localization
  • Error Estimation Method for Matrix Correlation-Based Wi-Fi Indoor Localization
저자명
Sun. Yong-Liang,Xu. Yu-Bin
간행물명
KSII Transactions on internet and information systems : TIIS
권/호정보
2013년|7권 11호|pp.2657-2675 (19 pages)
발행정보
한국인터넷정보학회
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정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.