Multi-cluster-center based filtering algorithm and its application to WLAN indoor positioning

Wireless local area network (WLAN) is developing to a ubiquitous technique in daily life. As a related product, WLAN based indoor positioning system is attracting more and more concern. Fingerprint is a mainstream method of wireless indoor positioning. However, it still has some shortcomings of that received signal strength (RSS) is multi-modal and sensitive to environmental factors. These characters would have a negative effect on the performance of positioning system. In this paper, a filtering algorithm based on multi-cluster-center is proposed. We make full use of 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, we use multiple sets of original WLAN signal samples and signal samples after filtering as the training input of positioning system respectively. After that, the results analysis is demonstrated. Simulation results show that it is a reliable algorithm to enhance the performance of WLAN indoor positioning.