Paper Info
Title
Smallest Enclosing Circle-Based Fingerprint Clustering And Modified-Wknn Matching Algorithm For Indoor Positioning
Abstract
Methods to cluster fingerprints based on Smallest-Enclosing-Circle (SEC) and to modify Weighted-K-Nearest-Neighbor (WKNN) matching algorithm for indoor fingerprint positioning system are proposed. Based on the approach to computing the smallest k-enclosing circle, the method proposed clusters fingerprints in database by introducing reference points' coordinates, instead of their received signal strength (RSS). This approach performs higher accuracy of positioning areas compared to conventional clustering algorithms, which are based on RSS. Meanwhile, this paper analyses the transmission characteristics of wireless signals in dense cluttered environments, and derives a novel path-loss-model-based weight computational method for WKNN matching algorithm. A modified-WKNN (M-WKNN) matching algorithm for indoor fingerprint positioning system is proposed and experiments are implemented in China National Grand Theatre. Results show that the location area accuracy using the proposed clustering algorithm is improved by 30% compared to that using K-means algorithm, and the positioning accuracy of M-WKNN is 11.9% and 29.1% higher than that of WKNN and KNN, respectively.
Year
Venue
Keywords
2016
2016 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN)
smallest enclosing circle, clustering algorithm, matching principle, fingerprint positioning
Field
DocType
ISSN
Computer vision,Algorithm design,Wireless,Fingerprint recognition,Fingerprint,Artificial intelligence,Engineering,Cluster analysis,RSS,Blossom algorithm,Positioning system
Conference
2162-7347
Citations 
PageRank 
References 
2
0.38
6
Authors
5
Name
Order
Citations
PageRank
Wen Liu142.12
Xiao Fu231.43
Zhongliang Deng3217.47
Lianming Xu472.17
Jichao Jiao5186.53