A Fuzzy Similarity Elimination Algorithm for Indoor Fingerprint Positioning. (13th August 2015)

• Record Type: Journal Article
• Authors: Chen, Yongle; Liu, Wei; Xiong, Yongping; Duan, Jing; Li, Zhi; Zhu, Hongsong
• Other Names: Cai Lin Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2015)
• Publication Date: 2015-08-13
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2015/753191
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : Fingerprint positioning can take advantage of existing WLAN to achieve indoor locations, which has been widely studied. We analyzed the corresponding positions distribution of similar fingerprints, and then found that the fuzzy similarity between fingerprints is the root cause of the larger errors existing. According to clusters distribution feature of corresponding positions of the similar fingerprints, we proposed a K -Means+ clustering algorithm to achieve fine-grained fingerprint positioning. Due to the K -Means+ algorithm failing to locate the positions of outliers, we also designed a linear sequence matching algorithm to improve the outliers positioning, and reduce the impact of fuzzy similarity. Experimental results illustrate that our algorithm can get a maximum positioning error less than 5 m, which outperforms other algorithms. Meanwhile, all the positioning errors over 4 m in our algorithm are less than 2%. The positioning accuracy has been improved significantly.