Research Article
Highly-Available Localization Techniques in Indoor Wi-Fi Environment: A Comprehensive Survey
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@INPROCEEDINGS{10.1007/978-3-030-00557-3_45, author={Mu Zhou and Oyungerel Bulgantamir and Yanmeng Wang}, title={Highly-Available Localization Techniques in Indoor Wi-Fi Environment: A Comprehensive Survey}, proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings}, proceedings_a={MLICOM}, year={2018}, month={10}, keywords={Wi-Fi localization RSS correlation Smooth filtering Neighbor matching Bayesian estimation}, doi={10.1007/978-3-030-00557-3_45} }- Mu Zhou
Oyungerel Bulgantamir
Yanmeng Wang
Year: 2018
Highly-Available Localization Techniques in Indoor Wi-Fi Environment: A Comprehensive Survey
MLICOM
Springer
DOI: 10.1007/978-3-030-00557-3_45
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.
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