A Fuzzy Similarity Elimination Algorithm for Indoor Fingerprint Positioning

Yongle Chen, Wei Liu, Yongping Xiong, Jing Duan, Zhi Li and Hongsong Zhu

International Journal of Distributed Sensor Networks, 2015, vol. 11, issue 8, 753191

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.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1155/2015/753191 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:11:y:2015:i:8:p:753191

DOI: 10.1155/2015/753191

Access Statistics for this article

More articles in International Journal of Distributed Sensor Networks
Bibliographic data for series maintained by SAGE Publications ().