Weight-RSS: A Calibration-Free and Robust Method for WLAN-Based Indoor Positioning | Zendy

Zengwei Zheng | Zendy; Yuanyi Chen | Zendy; Tao He | Zendy; Fei Li | Zendy; Dan Chen | Zendy

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Weight-RSS: A Calibration-Free and Robust Method for WLAN-Based Indoor Positioning

Author(s) -

Zengwei Zheng,

Yuanyi Chen,

Tao He,

Fei Li,

Dan Chen

Publication year - 2015

Publication title -

international journal of distributed sensor networks

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.324

H-Index - 53

eISSN - 1550-1477

pISSN - 1550-1329

DOI - 10.1155/2015/573582

Subject(s) - rss , computer science , fingerprint (computing) , variance (accounting) , calibration , fingerprint recognition , mobile device , relation (database) , real time computing , data mining , artificial intelligence , statistics , mathematics , world wide web , accounting , business

Wi-Fi fingerprinting has become a promising solution for indoor positioning with the rapid deployment of WLAN and the growing popularity of mobile devices. In fingerprint-based positioning, the received signal strengths (RSS) from WLAN access points (APs) usually are regarded as positioning fingerprint to label physical location. However, the RSS variance caused by heterogeneous devices and dynamic environmental status will significantly degrade the positioning accuracy. In this paper, we first show the RSS variance based on a real dataset and analyze the relation existing in the RSS raw values. Then, we utilize both the raw RSS values and their relation to construct a new stable and robust fingerprint for indoor positioning. Experiment results indicate that our method can solve the RSS variance problem without any manual calibration.

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