Low Human-Effort, Device-Free Localization with Fine-Grained Subcarrier Information

Abstract

Device-free localization of objects not equipped with RF radios is playing a critical role in many applications. This paper presents LIFS, a Low human-effort, device-free localization system with fine-grained subcarrier information, which can localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target’s location and thus the target can be localized by modelling the CSI measurements of multiple wireless links. However, due to rich multipath indoors, CSI can not be easily modelled. To deal with this challenge, our key observation is that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our CSI pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSI on the “clean” subcarriers can still be utilized for accurate localization. Without the need of knowing the majority transceivers’ locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios, respectively, outperforming the state-of-the-art systems.