Research Article
Application of Multi-Cluster-Center Based Filtering in WLAN Indoor Positioning
@INPROCEEDINGS{10.1109/ChinaCom.2011.6158194, author={Limin Li and Lin Ma and Yubin Xu and Jiayin Wang}, title={Application of Multi-Cluster-Center Based Filtering in WLAN Indoor Positioning}, proceedings={6th International ICST Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2012}, month={3}, keywords={rss filtering clustering wlan indoor positioning fingerprint}, doi={10.1109/ChinaCom.2011.6158194} }- Limin Li
Lin Ma
Yubin Xu
Jiayin Wang
Year: 2012
Application of Multi-Cluster-Center Based Filtering in WLAN Indoor Positioning
CHINACOM
IEEE
DOI: 10.1109/ChinaCom.2011.6158194
Abstract
Wireless local network (WLAN) is developing to a ubiquitous technique in our daily life recently. As a related product, WLAN based indoor positioning system is getting more and more attention. Fingerprint is a mainstream method of wireless indoor positioning, but it has the shortcomings of that received signal strength (RSS) is multi-modal and sensitive to environmental factors. These characters would adversely affect the performance of positioning system. To address this issue, a filtering algorithm based on multi-cluster-center is proposed in this paper. We use this algorithm to optimize the training samples at off-line phase to improve the performance of non-linear fitting with the fingerprint feature, and further enhance the positioning accuracy. Finally, the positioning accuracy before and after filtering is compared with multiple sets of real RSS samples. The simulation results show that it is a reliable algorithm to enhance the performance of WLAN indoor positioning.
