A Dynamic k-Nearest Neighbor Method for WLAN-Based Positioning Systems

ABSTRACT

The static k-Nearest Neighbor (k-NN) method for localization has limitations in accuracy due to the fixed k value in the algorithm. To address this problem, and achieve better accuracy, we propose a new dynamic k-Nearest Neighbor (Dk-NN) method in which the optimal k value changes based on the topologies and distances of its nearest neighbors. The proposed method has been validated using the WLAN-fingerprint data sets collected at COEX, one of the largest convention centers in Seoul, Korea. The proposed method significantly reduced both the mean error distances and the standard deviations of location estimations, leading to a significant improvement in accuracy by ~ 23% compared to the cluster filtered k-NN (CFK) method, and ~ 17% compared to the k-NN (k = 1) method.

Keywords:

• Positioning
• localization
• k-NN
• dynamic k-nearest neighbor
• WLAN fingerprint

Additional information

Funding

This work was supported partly by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT & Future Planning as Global Frontier Project [CISS-2012M3A6A6054195], and partly by the KAIST Institute (KI).