KNN-FCM hybrid algorithm for indoor location in WLAN

As a fingerprint match method, k-nearest neighbors (KNN) has been widely applied for indoor location in Wireless Local Area Networks (WLAN), but its performance is sensitive to number of neighbors k and positions of reference points (RPs). So fuzzy c-means (FCM) clustering algorithm is applied to improve KNN, which is the KNN-FCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN, k RPs are firstly chosen as the data samples of FCM based on received signal strength (RSS). Then, the k RPs are classified into different clusters through FCM based on RSS and the position coordinates. According to the rules proposed in this paper, some RPs are reselected for indoor location in order to improve the location precision. Simulation results indicate that the proposed KNN-FCM hybrid algorithm generally outperforms KNN when the location error is less than 2 m.