电子测量与仪器学报

周金海,吴耿俊,雷雯,常阳,周世镒.基于呼吸样本空间的超宽带雷达身份识别[J].电子测量与仪器学报,2022,36(1):118-125

基于呼吸样本空间的超宽带雷达身份识别

UWB radar identification based on breathing sample space

DOI：

中文关键词: 超宽带 UWB 雷达环境辅助生活生命体征身份识别卷积神经网络

英文关键词:ultra-wideband uwb radar ambient assisted life vital signs identity recognition convolutional neural network

基金项目:江省基础公益研究计划(LGF20F020014)、浙江省教育厅科研项目(Y201941858)、OPPO创新基金资助项目(CN8201807030008)、浙江大学自主科研资助项目(H20151111)资助

作者	单位
周金海	1.浙江大学信息与电子工程学院
吴耿俊	1.浙江大学信息与电子工程学院
雷雯	1.浙江大学信息与电子工程学院
常阳	1.浙江大学信息与电子工程学院
周世镒	1.浙江大学信息与电子工程学院

Author	Institution
Zhou Jinhai	1.College of Information Science and Electronic Engineering, Zhejiang University
Wu Gengjun	1.College of Information Science and Electronic Engineering, Zhejiang University
Lei Wen	1.College of Information Science and Electronic Engineering, Zhejiang University
Chang Yang	1.College of Information Science and Electronic Engineering, Zhejiang University
Zhou Shiyi	1.College of Information Science and Electronic Engineering, Zhejiang University

摘要点击次数: 1056

全文下载次数: 989

中文摘要:

为了解决传统雷达呼吸身份识别依赖人工预定义特征的问题,提出了一种基于呼吸样本空间(BSS)的超宽带(UWB) 雷达身份识别算法。算法首先通过奇异值分解(SVD)对 UWB 雷达人体呼吸回波中杂波进行滤除,然后根据回波将目标跨距离单元的呼吸信号构建为包含时距信息的 BSS 序列,最后利用卷积神经网络(CNN)对 BSS 进行建模以获取目标分类结果。在室内场景实验中,对 4 人的身份识别准确率为 84. 64%。对比结果表明,所提出的算法对不同个体所具有的独特呼吸特征具有不错的区分能力。

英文摘要:

In order to solve the problem that traditional radar breathing identification relies on artificial predefined features, an ultrawideband (UWB) radar identification algorithm based on breath sample space (BSS) is proposed. The algorithm uses singular value decomposition (SVD) to filter out the clutter in the UWB radar human respiratory echo; the target cross-range respiratory signal is constructed as a BSS sequence containing time-distance information according to the echo; the convolutional neural network (CNN) is used to model the BSS to obtain the target classification results. In the indoor scene experiment, the identification accuracy of the four persons was 84. 64%. The comparison results show that the proposed algorithm has a good ability to distinguish the unique breathing characteristics of different individuals.

查看全文查看/发表评论下载PDF阅读器