This paper focuses on WiFi indoor positioning based on received signal strength, and weighed K-nearest neighbor (WKNN) algorithm is the most classic position estimation strategy. However, locating across the entire fingerprint database takes a lot of time. In this paper, a clustering algorithm based on Self-Organization Map (SOM) is proposed to shorten the positioning time. Meanwhile, an improved WKNN algorithm is proposed to further increase the positioning accuracy. The experiment results show that the positioning time is effectively cut down after clustering and the average positioning error of the proposed algorithm is 1.18 m, which can achieve high accuracy in indoor environment.
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