A New Indoor Positioning Approach based on Weighted K-Nearest Algorithm

Abstract

Many contemporary technological services rely heavily on precise location data within smartphone applications, making accuracy a crucial aspect of indoor positioning systems. However, the variability in received signal strength (RSS) poses a challenge for achieving exact locations in Wi-Fi indoor positioning algorithms. Traditional weighted k-nearest neighbor (WkNN) techniques typically utilize RSS spatial distance for selecting reference points (RPs) to estimate locations. To enhance position accuracy, this study introduces a novel indoor positioning method based on WkNN. By incorporating three geometrical distances of RSS (physical, spatial, and Canberra), this approach selects RPs and conducts position estimation using a fusion weighted strategy based on these distances. Experimental findings indicate that the newly proposed method outperforms the nearest neighbor (NN) technique. Moreover, comparative investigations demonstrate its superiority over k-nearest neighbor (kNN) and weighted k-nearest neighbor (WkNN) algorithms. Compared to NN, kNN, and WkNN algorithms, this novel technique improves positioning accuracy by approximately 49.9%, 32%, and 25%, respectively.