A Feature-Scaling-Based <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-Nearest Neighbor Algorithm for Indoor Positioning Systems

TLDR

WiFi fingerprint indoor positioning systems rely on matching RSS vectors to reference fingerprints, yet current methods ignore that equal RSS differences at different signal levels do not correspond to equal geometric distances in complex indoor environments. This study introduces a feature‑scaling‑based k‑nearest neighbor (FS‑kNN) algorithm to enhance localization accuracy. FS‑kNN employs an RSS‑level‑based scaling model that assigns weighting factors to RSS differences, thereby refining the effective signal distance calculation between a mobile station’s RSS vector and reference fingerprints. Experimental evaluation shows FS‑kNN achieves an average location error of 1.70 m, outperforming existing approaches.

Abstract

With the increasing popularity of WLAN infrastructure, WiFi fingerprint-based indoor positioning systems have received considerable attention recently. Much existing work in this aspect adopts classification techniques that match a vector of radio signal strengths (RSSs) reported by a mobile station (MS) to pretrained reference fingerprints sampled from different access points (APs) at different reference points (RPs) with known positions. However, in the calculation of signal distances between different RSS vectors, existing techniques fail to consider the fact that equal RSS differences at different RSS levels may not mean equal differences in geometrical distances in complex indoor environment. To address this issue, in this paper, we propose a feature-scaling-based k-nearest neighbor (FS-kNN) algorithm for achieving improved localization accuracy. In FS-kNN, we build a novel RSS-level-based FS model, which introduces RSS-level-based scaling weights in the computation of effective signal distances between signal vector reported by a MS and reference fingerprints in a radio map. Experimental results show that FS-kNN can achieve an average location error as low as 1.70 m, which is superior to existing work.