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• W2895856371 abstract "With the increasing interests on received signal strength (RSS) fingerprint-based Wi-Fi localization, the requirement of recording reliable and accurate RSS fingerprints for radio map construction becomes a significant concern. The neighbor matching and Bayesian estimation is recognized as the two most representative algorithms for RSS fingerprint-based indoor Wi-Fi localization. To guarantee the accuracy performance of neighbor matching and Bayesian estimation algorithms, we introduce several method to eliminate RSS sample noise for the sake of improving the distance dependency of Wi-Fi RSS fingerprints." @default.
• W2895856371 created "2018-10-26" @default.
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• W2895856371 date "2018-01-01" @default.
• W2895856371 modified "2023-10-06" @default.
• W2895856371 title "Highly-Available Localization Techniques in Indoor Wi-Fi Environment: A Comprehensive Survey" @default.
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• W2895856371 doi "https://doi.org/10.1007/978-3-030-00557-3_45" @default.
• W2895856371 hasPublicationYear "2018" @default.
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