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平面环境与楼梯环境下基于 Wi-Fi 信道状态信息的室内运动检测。

Indoor Motion Detection Using Wi-Fi Channel State Information in Flat Floor Environments Versus in Staircase Environments.

机构信息

School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.

Department of Mathematics and Computer Science, Changsha University, Changsha 410083, China.

出版信息

Sensors (Basel). 2018 Jul 6;18(7):2177. doi: 10.3390/s18072177.

Abstract

Recently, Wi-Fi channel state information (CSI) motion detection systems have been widely researched for applications in human health care and security in flat floor environments. However, these systems disregard the indoor context, which is often complex and consists of unique features, such as staircases. Motion detection on a staircase is also meaningful and important for various applications, such as fall detection and intruder detection. In this paper, we present the difference in CSI motion detection in flat floor and staircase environments through analysing the radio propagation model and experiments in real settings. For comparison in the two environments, an indoor CSI motion detection system is proposed with several novel methods including correlation-based fusion, moving variance segmentation (MVS), Doppler spread spectrum to improve the system performance, and a correlation check to reduce the implementation cost. Compared with existing systems, our system is validated to have a better performance in both flat floor and staircase environments, and further utilized to verify the superior CSI motion detection performance in staircase environments versus flat floor environments.

摘要

最近,Wi-Fi 信道状态信息 (CSI) 运动检测系统在平面地板环境中的人体健康护理和安全应用方面得到了广泛的研究。然而,这些系统忽略了室内环境,室内环境通常很复杂,包含独特的特征,如楼梯。楼梯上的运动检测对于各种应用也具有重要意义,如跌倒检测和入侵检测。在本文中,我们通过分析无线电传播模型和真实环境中的实验,研究了平面地板和楼梯环境中 CSI 运动检测的差异。为了在这两种环境中进行比较,我们提出了一种室内 CSI 运动检测系统,该系统采用了一些新方法,包括基于相关的融合、运动方差分割 (MVS)、多普勒扩展频谱,以提高系统性能,并采用相关检查来降低实现成本。与现有系统相比,我们的系统在平面地板和楼梯环境中都验证了具有更好的性能,并且进一步用于验证楼梯环境中 CSI 运动检测性能优于平面地板环境。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d6b/6068568/48af5c58dae0/sensors-18-02177-g001.jpg

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