Abstract—In this paper, we proposed a RSSI-Probability transformation algorithm for radio map construction that improves positioning accuracy and AP (Access Point) weight clustering that reduces the computational burden. To verify the performance, we developed a positioning system called LocNeedle. The experimental results show that our algorithm achieves good localization accuracy and reduces computational burden of online phase.
Index Terms—Wi-Fi fingerprint localization, RSSI probability distribution, AP weight clustering.
Peng Tang, Zhiqing Huang and Jun Lei are with the School of Software Engineering at Beijing University of Technology, Chaoyang District, Beijing China, 100124 (e-mail: tangpeng_tp@emails.bjut.edu.cn, zqhuang@bjut.edu.cn, jun_lei@emails.bjut.edu.cn).
Yue Guo is with Patent Office at Patent Examination Cooperation, Tianjin Center, SIPO, China (e-mail: 379141904@qq.com).
[PDF]
Cite:Peng Tang, Zhiqing Huang, Jun Lei, and Yue Guo, "Wi-Fi Fingerprint Localization Using RSSI-Probability Radio Map and AP Weight Clustering," Journal of Advances in Computer Networks vol. 4, no. 2, pp. 121-124, 2016.
