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- W3171100602 abstract "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." @default.
- W3171100602 created "2021-06-22" @default.
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- W3171100602 date "2021-04-01" @default.
- W3171100602 modified "2023-10-14" @default.
- W3171100602 title "WKNN indoor Wi-Fi localization method using k-means clustering based radio mapping" @default.
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- W3171100602 doi "https://doi.org/10.1109/vtc2021-spring51267.2021.9448961" @default.
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