Indoor localization techniques using Wi-Fi fingerprints have become prevalent in recent years because of their cost-effectiveness\nand high accuracy. The most common algorithm adopted for Wi-Fi fingerprinting is weighted K-nearest neighbors (WKNN),\nwhich calculates K-nearest neighboring points to a mobile user. However, existing WKNN cannot effectively address the problems\nthat there is a difference in observed AP sets during offline and online stages and also not all the K neighbors are physically close\nto the user. In this paper, similarity coefficient is used to measure the similarity of AP sets, which is then combined with radio\nsignal strength values to calculate the fingerprint distance. In addition, isolated points are identified and removed before clustering\nbased on semi-supervised affinity propagation. Real-world experiments are conducted on a university campus and results show the\nproposed approach does outperform existing approaches.
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