A DYNAMIC k-NEAREST NEIGHBOR METHOD FOR WLAN-BASED POSITIONING SYSTEMS

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The static k-Nearest Neighbor (k-NN) method for localization has limitations in accuracy due to the fixed k value in the algorithm. To address this problem, and achieve better accuracy, we propose a new dynamic k-Nearest Neighbor (Dk-NN) method in which the optimal k value changes based on the topologies and distances of its nearest neighbors. The proposed method has been validated using the WLAN-fingerprint data sets collected at COEX, one of the largest convention centers in Seoul, Korea. The proposed method significantly reduced both the mean error distances and the standard deviations of location estimations, leading to a significant improvement in accuracy by similar to 23% compared to the cluster filtered k-NN (CFK) method, and similar to 17% compared to the k-NN (k = 1) method
Publisher
TAYLOR & FRANCIS INC
Issue Date
2016-06
Language
English
Article Type
Article
Citation

JOURNAL OF COMPUTER INFORMATION SYSTEMS, v.56, no.4, pp.295 - 300

ISSN
0887-4417
DOI
10.1080/08874417.2016.1164000
URI
http://hdl.handle.net/10203/213033
Appears in Collection
CS-Journal Papers(저널논문)
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