TY - GEN
T1 - DNN-based Outdoor NLOS Human Detection Using IEEE 802.11ac WLAN Signal
AU - Takahashi, Ryo
AU - Ishida, Shigemi
AU - Fukuda, Akira
AU - Murakami, Tomoki
AU - Otsuki, Shinya
N1 - Funding Information:
This work was partially supported by JSPS KAKENHI Grant Number 15H05708 and JST ACT-I Grant Number JPMJPR18U2.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
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U2 - 10.1109/SENSORS43011.2019.8956943
DO - 10.1109/SENSORS43011.2019.8956943
M3 - Conference contribution
AN - SCOPUS:85078696550
T3 - Proceedings of IEEE Sensors
BT - 2019 IEEE Sensors, SENSORS 2019 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Sensors, SENSORS 2019
Y2 - 27 October 2019 through 30 October 2019
ER -