WKNN indoor Wi-Fi localization method using k-means clustering based radio mapping

Wifi fingerprinting using received signal strength has been widely studied for indoor localization. Classic similarity-based methods like weighted K-nearest neighbor (WKNN) localize targets by searching for the best matching fingerprint in the dataset. Performance of these methods suffers from RSS variance and they are slow under a large size of fingerprint dataset. In this paper, we propose a WKNN localization strategy using k-means clustering radio mapping that balances localization precision and computational complexity.

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