DocumentCode :
3700489
Title :
Identification and mitigation of NLOS based on channel state information for indoor WiFi localization
Author :
Xiong Cai;Xiaohui Li;Ruiyang Yuan;Yongqiang Hei
Author_Institution :
State Key Laboratory of Integrated Service Networks, Xidian University Xi´an, Shaanxi 710071, China
fYear :
2015
Firstpage :
1
Lastpage :
5
Abstract :
Indoor localization could benefit greatly from non-line-of-sight (NLOS) identification and mitigation, since the major challenge for WiFi indoor ranging-based localization technologies are multipath and NLOS. NLOS identification and mitigation on commodity WiFi devices, however, is challenge due to limited bandwidth and coarse multipath resolution with mere MAC layer RSSI. In this study, we explore and exploit the finer-grained PHY layer channel state information (CSI) to identify and mitigate NLOS. Key to our approach is exploiting several statistical features of CSI, which are proved to be particularly effective. Approach based on machine learning is proposed to identify NLOS and mitigate NLOS error. Experiment results in various indoor scenarios with severe interferences demonstrate that the proposed approach outperform previous threshold-based approaches and mitigate the impact of NLOS conditions perfectly.
Keywords :
"Nonlinear optics","IEEE 802.11 Standard","Support vector machines","Power measurement","Phase measurement","Feature extraction","Data models"
Publisher :
ieee
Conference_Titel :
Wireless Communications & Signal Processing (WCSP), 2015 International Conference on
Type :
conf
DOI :
10.1109/WCSP.2015.7341172
Filename :
7341172
Link To Document :
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