Abstract
In this paper, we rely on the neighborhood relations of the physically adjacent Reference Points (RPs) to construct a physical neighborhood database with the purpose of enhancing the accuracy of the Receive Signal Strength (RSS) fingerprint based localization algorithms in Wireless Local Area Network (WLAN) environment. First of all, based on the Most Adjacent Points (MAPs) and their corresponding Physically Adjacent Points (PAPs), we construct the Feature Groups (FGs), and then calculate the New Reference Point (NRP) with respect to each FG. Second, the RSS at each NRP is estimated by using the least square method based surface interpolation algorithm. Finally, we apply the K Nearest Neighbor (KNN), Weighted KNN (WKNN), and Bayesian inference algorithms to locate the target. The experimental results show that the proposed integrated database construction helps a lot in improving the localization accuracy of the widely-used KNN, WKNN, and Bayesian inference algorithms.
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Acknowledgment
This work was supported in part by the Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), National Natural Science Foundation of China (61301126), Special Fund of Chongqing Key Laboratory (CSTC), and Fundamental and Frontier Research Project of Chongqing (cstc2013jcyjA40041 and cstc2013jcyjA40032) and Postgraduate Scientific Research and Innovation Project of Chongqing (CYS16157).
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhang, Q., Zhou, M., Tian, Z. (2017). Accuracy Enhancement with Integrated Database Construction for Indoor WLAN Localization. In: Xin-lin, H. (eds) Machine Learning and Intelligent Communications. MLICOM 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-319-52730-7_17
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DOI: https://doi.org/10.1007/978-3-319-52730-7_17
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