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SWiLoc:融合智能手机传感器与WiFi信道状态信息以实现精确室内定位

SWiLoc: Fusing Smartphone Sensors and WiFi CSI for Accurate Indoor Localization.

作者信息

Mottakin Khairul, Davuluri Kiran, Allison Mark, Song Zheng

机构信息

Department of Computer and Information Science, University of Michigan-Dearborn, Dearborn, MI 48128, USA.

College of Innovation and Technology, University of Michigan-Flint, Flint, MI 48502, USA.

出版信息

Sensors (Basel). 2024 Sep 30;24(19):6327. doi: 10.3390/s24196327.

DOI:10.3390/s24196327
PMID:39409367
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11479186/
Abstract

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone's direction and the user's actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (martphone and Fi-based alization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user's walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system's applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

摘要

航位推算(Dead reckoning)是一种很有前景但常常被忽视的基于智能手机的室内定位技术,它依靠手机内置传感器来计算步数并估算行走方向,无需大量部署传感器或地标。然而,手机方向与用户实际移动方向之间的偏差可能导致方向估计不可靠以及位置跟踪不准确。为了解决这个问题,本文介绍了SWiLoc(基于智能手机和Fi的定位),这是一种增强型方向校正系统,它将被动式WiFi感知与基于智能手机的感知相结合以形成校正区域。我们的两阶段方法在用户穿过校正区域时准确测量其行走方向,并在区域外进一步优化连续的方向估计,从而实现连续且可靠的跟踪。除了方向校正,SWiLoc还通过纳入一种利用校正后的方向来实现精确用户定位的定位技术来扩展其功能。这一扩展显著增强了系统在高精度定位任务中的适用性。此外,我们基于菲涅尔区的创新方法利用独特的硬件配置和基本几何模型,即使在行走方向不可靠的场景中也能确保准确且稳健的方向估计。我们在两个真实环境中对SWiLoc进行评估,评估其在环境变化、手机方向、行走方向和距离等不同条件下的性能。我们全面的实验表明,SWiLoc在行走方向估计中的平均第75百分位数误差为8.89度,在位置估计中的第80百分位数误差为1.12米。与现有的最先进方法相比,这些数字分别代表方向估计误差和位置估计误差降低了64%和49%。

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