基于深度学习的人体目标分析

Yuan He, Xinyu Li, Xiaojun Jing
{"title":"基于深度学习的人体目标分析","authors":"Yuan He, Xinyu Li, Xiaojun Jing","doi":"10.5772/INTECHOPEN.81592","DOIUrl":null,"url":null,"abstract":"In this chapter, we describe methods toward deep learning-based human target analy- sis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are dem onstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration.","PeriodicalId":410325,"journal":{"name":"UWB Technology and its Applications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Toward Deep Learning-Based Human Target Analysis\",\"authors\":\"Yuan He, Xinyu Li, Xiaojun Jing\",\"doi\":\"10.5772/INTECHOPEN.81592\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this chapter, we describe methods toward deep learning-based human target analy- sis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are dem onstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration.\",\"PeriodicalId\":410325,\"journal\":{\"name\":\"UWB Technology and its Applications\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"UWB Technology and its Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5772/INTECHOPEN.81592\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"UWB Technology and its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5772/INTECHOPEN.81592","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

在本章中,我们描述了基于深度学习的人类目标分析方法。首先介绍了雷达信号二维和三维域的人体目标分析。此外,还介绍了超宽带雷达用于人体目标分析的距离-多普勒曲面。距离-多普勒曲面的构建包括距离-多普勒成像、自适应阈值检测和等值面提取。与微多普勒曲线和高分辨率距离曲线相比,距离多普勒曲面同时包含距离、多普勒和时间信息。设计了基于椭球体的人体运动模型进行验证。对模拟不同人类活动的距离-多普勒曲面进行了说明和讨论。随着深度学习的迅速兴起,雷达目标识别的发展也得到了加速。我们描述了几种用于人类目标分析的深度学习算法。最后,列出了一些未来研究的考虑,以激发灵感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward Deep Learning-Based Human Target Analysis
In this chapter, we describe methods toward deep learning-based human target analy- sis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are dem onstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信