Identification and mitigation of NLOS based on channel state information for indoor WiFi localization

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.