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

  • Mu Zhou
  • Oyungerel BulgantamirEmail author
  • Yanmeng Wang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)


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.


Wi-Fi localization RSS correlation Smooth filtering Neighbor matching Bayesian estimation 



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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Copyright information

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

Authors and Affiliations

  1. 1.Chongqing Key Lab of Mobile Communications TechnologyChongqing University of Posts and TelecommunicationsChongqingChina

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