An improved WiFi positioning method based on fingerprint clustering and signal weighted euclidean distance

Abstract

WiFi fingerprint positioning has beenwidely used in the indoor positioning field. Theweighed K-nearest neighbor (WKNN) algorithm is one of the most widely used deterministic algorithms. The traditionalWKNN algorithm uses Euclidean distance or Manhattan distance between the received signal strengths (RSS) as the distance measure to judge the physical distance between points. However, the relationship between the RSS and the physical distance is nonlinear, using the traditional Euclidean distance or Manhattan distance to measure the physical distance will lead to errors in positioning. In addition, the traditional RSS-based clustering algorithm only takes the signal distance between the RSS as the clustering criterion without considering the position distribution of reference points (RPs). Therefore, to improve the positioning accuracy,we propose an improvedWiFi positioningmethod based on fingerprint clustering and signal weighted Euclidean distance (SWED). The proposed algorithm is tested by experiments conducted in two experimental fields. The results indicate that compared with the traditional methods, the proposed position label-assisted (PL-assisted) clustering result can reflect the position distribution of RPs and the proposed SWED-basedWKNN (SWED-WKNN) algorithm can significantly improve the positioning accuracy.