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Feature Learning for Fingerprint-Based Positioning in Indoor Environment

Author

Listed:
  • Zengwei Zheng
  • Yuanyi Chen
  • Tao He
  • Lin Sun
  • Dan Chen

Abstract

Recent years have witnessed a growing interest in using Wi-Fi received signal strength for indoor fingerprint-based positioning. However, previous study about this problem has primarily faced two main challenges. One is that positioning fingerprint feature using received signal strength is unstable due to heterogeneous devices and dynamic environment status, which will greatly degrade the positioning accuracy. Another is that some improved positioning fingerprint features will suffer the curse of dimensionality in online positioning. In this paper, we designed a novel positioning fingerprint feature using the segment similarity of Wi-Fi access points, considering both the received signal strength value and the Wi-Fi access point. Based on this designed fingerprint feature, we proposed a two-stage positioning algorithm for indoor fingerprint-based positioning. Experiment results indicate that our proposed positioning methodology can not only achieve better positioning performance but also consume less positioning time compared to three baseline methods.

Suggested Citation

  • Zengwei Zheng & Yuanyi Chen & Tao He & Lin Sun & Dan Chen, 2015. "Feature Learning for Fingerprint-Based Positioning in Indoor Environment," International Journal of Distributed Sensor Networks, , vol. 11(10), pages 452590-4525, October.
    • Handle: RePEc:sae:intdis:v:11:y:2015:i:10:p:452590
    DOI: 10.1155/2015/452590
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