A Feature Scaling Based k-Nearest Neighbor Algorithm for Indoor Positioning System

With the increasing popularity of wireless local area network infrastructure, WiFi fingerprint based indoor positioning systems have received considerable attention in recent years. In the literature, most existing work in this area focuses on techniques that match the vector of radio signal strength (RSS) values reported by a mobile device to the fingerprints collected at predetermined reference points (RPs) by comparing the similarity (measured based on RSS difference) between them. However, these existing techniques fail to consider the fact that equal RSS differences at different RSS levels may not mean equal distances in reality. To address this issue, in this paper, we propose a feature scaling based k-nearest neighbor algorithm (FS-kNN) for improved localization accuracy. In FS-kNN, we build a novel RSS-based feature scaling model, which introduces signal-level-scaled weights in the calculation of effective signal distance between signal vector reported by mobile device and existing fingerprints. Experimental results show that FS-kNN can achieve an average error distance as low as 1.93 meters, which is superior to previous work.