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Highly-Available Localization Techniques in Indoor Wi-Fi Environment: A Comprehensive Survey

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Machine Learning and Intelligent Communications (MLICOM 2018)

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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Acknowledgments

The authors wish to thank the reviewers for the careful review and valuable suggestions. This work is supported in part by the National Natural Science Foundation of China (61771083,61704015), Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), Special Fund of Chongqing Key Laboratory (CSTC), Fundamental and Frontier Research Project of Chongqing (cstc2017jcyjAX0380, cstc2015jcyjBX0065), Scientific and Technological Research Foundation of Chongqing Municipal Education Commission (KJ1704083), University Outstanding Achievement Transformation Project of Chongqing (KJZH17117), and Postgraduate Scientific Research and Innovation Project of Chongqing (CYS17221).

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Correspondence to Oyungerel Bulgantamir .

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhou, M., Bulgantamir, O., Wang, Y. (2018). Highly-Available Localization Techniques in Indoor Wi-Fi Environment: A Comprehensive Survey. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_45

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  • DOI: https://doi.org/10.1007/978-3-030-00557-3_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00556-6

  • Online ISBN: 978-3-030-00557-3

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