UWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario

Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments,...

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Autores principales:	Li Zhao, Ma Yangyang, Zhang Yang, Liang Fulai, Yu Xiao, Qi Fugui, Lv Hao, Lu Guohua, Wang Jianqi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Correlation coefficient distinguishing between human and animal multipath effect post-disaster rescue ultra-wideband (UWB) radar Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/31d939606a94445888a4a72a85543eec
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spelling	oai:doaj.org-article:31d939606a94445888a4a72a85543eec2021-11-24T00:02:32ZUWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario2169-353610.1109/ACCESS.2021.3128156https://doaj.org/article/31d939606a94445888a4a72a85543eec2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9615073/https://doaj.org/toc/2169-3536Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments, such that the effectiveness of these approaches cannot be guaranteed in rescue situations. This study describes experiments under actual post-disaster emergency rescue scenarios, for which the signal-to-noise ratio of UWB radar is seriously degraded owing to multipath effects and a complicated ruin environment. Four distinguishing features are extracted from aspects of wavelet entropy, correlation coefficient, and energy to classify humans from animals. Analysis of feature effectiveness showed that each feature could identify humans from animals individually. The largest difference between humans and animals was found in a feature which combines advantages of the correlation coefficient and energy simultaneously. There was no overlap between the human and animal values for this feature among the 20 sets of radar data collected. This is the first attempt to distinguish humans from animals in an actual post-disaster trapped condition, and it yielded four features of strong classification ability. We envision this study to advance real-world applicability of UWB radar in post-disaster emergency rescue.Li ZhaoMa YangyangZhang YangLiang FulaiYu XiaoQi FuguiLv HaoLu GuohuaWang JianqiIEEEarticleCorrelation coefficientdistinguishing between human and animalmultipath effectpost-disaster rescueultra-wideband (UWB) radarElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154347-154354 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Correlation coefficient distinguishing between human and animal multipath effect post-disaster rescue ultra-wideband (UWB) radar Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Correlation coefficient distinguishing between human and animal multipath effect post-disaster rescue ultra-wideband (UWB) radar Electrical engineering. Electronics. Nuclear engineering TK1-9971 Li Zhao Ma Yangyang Zhang Yang Liang Fulai Yu Xiao Qi Fugui Lv Hao Lu Guohua Wang Jianqi UWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario
description	Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments, such that the effectiveness of these approaches cannot be guaranteed in rescue situations. This study describes experiments under actual post-disaster emergency rescue scenarios, for which the signal-to-noise ratio of UWB radar is seriously degraded owing to multipath effects and a complicated ruin environment. Four distinguishing features are extracted from aspects of wavelet entropy, correlation coefficient, and energy to classify humans from animals. Analysis of feature effectiveness showed that each feature could identify humans from animals individually. The largest difference between humans and animals was found in a feature which combines advantages of the correlation coefficient and energy simultaneously. There was no overlap between the human and animal values for this feature among the 20 sets of radar data collected. This is the first attempt to distinguish humans from animals in an actual post-disaster trapped condition, and it yielded four features of strong classification ability. We envision this study to advance real-world applicability of UWB radar in post-disaster emergency rescue.
format	article
author	Li Zhao Ma Yangyang Zhang Yang Liang Fulai Yu Xiao Qi Fugui Lv Hao Lu Guohua Wang Jianqi
author_facet	Li Zhao Ma Yangyang Zhang Yang Liang Fulai Yu Xiao Qi Fugui Lv Hao Lu Guohua Wang Jianqi
author_sort	Li Zhao
title	UWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario
title_short	UWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario
title_full	UWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario
title_fullStr	UWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario
title_full_unstemmed	UWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario
title_sort	uwb radar features for distinguishing humans from animals in an actual post-disaster trapped scenario
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/31d939606a94445888a4a72a85543eec
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UWB Radar Features for Distinguishing Humans From Animals in an Actual Post-Disaster Trapped Scenario

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