Open AccessNLOS identification and mitigation based on channel state information for indoor WiFi localisation
Author(s) -
Li Xiaohui,
Cai Xiong,
Hei Yongqiang,
Yuan Ruiyang
Publication year - 2017
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2016.0562
Subject(s) - non line of sight propagation , computer science , multipath propagation , channel state information , physical layer , identification (biology) , wireless , channel (broadcasting) , real time computing , computer network , telecommunications , botany , biology
Indoor localisation could benefit greatly from non‐line‐of‐sight (NLOS) identification and mitigation, since the major challenge for WiFi indoor ranging‐based localisation technologies is multipath and NLOS. NLOS identification and mitigation on commodity WiFi devices, however, are challenges due to limited bandwidth and coarse multipath resolution with mere MAC layer received signal strength index. In this study, the authors explore and exploit the finer‐grained PHY layer channel state information (CSI) to identify and mitigate NLOS. Key to the authors’ approach is exploiting several statistical features of CSI, which are proved to be particularly effective. The approaches, NLOS identification support vector machine (NISVM) and related channel information regression model (RCIRM), based on machine learning are proposed to identify NLOS and mitigate NLOS error, respectively. Experiment results in various indoor scenarios with severe interferences demonstrated an overall NLOS identification rate of 94.12% with a false alarm rate of 5.88% and a better mitigation performance.