Application of multi-cluster-center based filtering in WLAN indoor positioning

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 nonlinear 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.