DNN-based Outdoor NLOS Human Detection Using IEEE 802.11ac WLAN Signal

Ryo Takahashi, Shigemi Ishida, Akira Fukuda, Tomoki Murakami, Shinya Otsuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, WLAN-based wireless sensing technologies, which utilize WLAN devices widely used in many environments, have been focused because of their low deployment cost. We have presented an outdoor human detector using IEEE802.11ac channel state information (CSI) for line-of-sight (LOS) scenarios in our previous work [1]. In this paper, we extend our previous work and present a CSI-based human detection system for outdoor non-line-of-sight (NLOS) scenarios. The key idea is to utilize CSI retrieved by multiple devices and extracted key features using principal component analysis (PCA) for sensing to avoid unstable detection performance. Experimental evaluations revealed that our human detection system for NLOS scenarios successfully located a human with an accuracy of 99.58 % using four WLAN stations.

Original languageEnglish
Title of host publication2019 IEEE Sensors, SENSORS 2019 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728116341
DOIs
Publication statusPublished - Oct 2019
Event18th IEEE Sensors, SENSORS 2019 - Montreal, Canada
Duration: Oct 27 2019Oct 30 2019

Publication series

NameProceedings of IEEE Sensors
Volume2019-October
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229

Conference

Conference18th IEEE Sensors, SENSORS 2019
CountryCanada
CityMontreal
Period10/27/1910/30/19

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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    Takahashi, R., Ishida, S., Fukuda, A., Murakami, T., & Otsuki, S. (2019). DNN-based Outdoor NLOS Human Detection Using IEEE 802.11ac WLAN Signal. In 2019 IEEE Sensors, SENSORS 2019 - Conference Proceedings [8956943] (Proceedings of IEEE Sensors; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SENSORS43011.2019.8956943