LiFS: low human-effort, device-free localization with fine-grained subcarrier information
By Ju Wang, Jie Xiong, Hongbo Jiang, Kyle Jamieson, Xiaojiang Chen, Dingyi Fang, Chen Wang. ACM/IEEE International Conference on Mobile Computing and Networking, 2016
303 citations recorded by the source corpus.
Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization. We design, implement and evaluate LiFS with extensive experiments in three different environments. Without 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. Besides single target localization, LiFS is able to differentiate two sparsely-located targets and localize each of them at a high accuracy.
Subfields: Electrical and Electronic Engineering, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/01fb0eb6a8a8055342d1e85d5e01a80fc98b842a
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Citations in the corpus
- WiFi Sensing with Channel State Information (2019, methodology): “LiFS [93] SE: Thresholding M: Fresnel Zone Model, DTW, Gradient Descent, Genetic Algorithm Device-Free Human Localization Median Accuracy: 0.”
- Towards 3D human pose construction using wifi (2020, background): “…are conducted to make use of WiFi devices to enable various sensing tasks, including indoor localization and tracking [5, 22, 25, 33, 39, 42, 48–50, 52, 53, 65], walking speed estimation [17, 43], breathing and heartbeats inference [6, 55, 58, 61], human identification [16, 43, 54],…”
- Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi (2021, methodology): “Since existing works [18], [20], [28] have pushed the limit of Wi-Fi-based passive tracking application into decimeter level, Widar3.”
- Widar: Decimeter-Level Passive Tracking via Velocity Monitoring with Commodity Wi-Fi (2017, methodology): “LiFS [16] requires dense deployment of devices to ensure targets always sufficiently close to the LoS of some links.”
- Precise Power Delay Profiling with Commodity Wi-Fi (2015, background): “site survey, and no sophisticated AP hardware [1, 6, 9, 47, 48].”
- Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi (2019, background): “In general, the last estimation of location and the last estimation of moving direction can be provided by tracking systems[26, 33, 41], as the location and orientation of the user in Widar3.”
- mD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking (2018, background): “Wi-Fi RSSI/CSI signature based passive localization systems [1, 5, 38, 39, 41, 45, 52] usually rely on high-density deployments which are not realistic for largescale deployments.”