摘要
穿墙目标检测在安防、智能家居和应急救援等方面具有重要的应用前景。目前,基于Wi-Fi的穿墙目标检测方法需在检测区域内安装额外设备,或需对硬件进行复杂改动。针对上述问题,提出一种新颖的基于Wi-Fi穿墙雷达的移动目标检测系统WiTWR,在检测区域内无附加设备并无需对硬件进行任何改动的情况下,实现墙后无源移动目标检测。对接收信道状态信息(channel state information,CSI)构建接收信号模型并对其相位进行修正;设计一种干扰抑制算法对墙面强反射信号进行抑制,并利用小波去噪对噪声干扰进行抑制;对子载波进行挑选并分别提取CSI在有无目标情况下的幅值和相位特征,在此基础上,利用支持向量机(support vector machine,SVM)构造目标有无检测分类器,从而实现穿墙目标检测。实验结果表明,提出的基于Wi-Fi穿墙雷达的目标检测方法准确率能达到85%以上。
Through-the-wall target detection has important application prospects in security,smart home and emergency rescue.Existing wireless Wi-Fi based through-the-wall target detection methods require additional devices to be installed in the detection area,or complex modifications of the hardware are required.For the insufficiencies of existing Wi-Fi based through-the-wall target detection,we propose a novel through-the-wall moving target detection system based on commercial Wi-Fi devices(WiTWR),which can realize the passive detection of moving target behind the wall.Firstly,the received channel state information(CSI)is modeled and the phase error is cancelled out.Then,the wall clutter is mitigated by the proposed clutter mitigation method and the noise is mitigated by the wavelet denoising.Finally,the amplitude and phase features of CSI in the case of presence and absence of targets are extracted based on the subcarrier selection,respectively.And the support vector machine(SVM)is utilized to construct the target detection classifier.Experiments show that the detection accuracy rate of the proposed algorithm in this paper can reach more than 85%.
作者
杨小龙
何艾琳
周牧
蒋青
田增山
YANG Xiaolong;HE Ailin;ZHOU Mu;JIANG Qing;TIAN Zengshan(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065;Key Lab of Mobile Communications Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065)
出处
《重庆邮电大学学报（自然科学版）》
CSCD
北大核心
2020年第1期74-84,共11页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金
国家自然科学基金(61771083,61704015)
重庆市教委科学技术研究项目(KJQN201800625)
长江学者和创新团队发展计划基金(IRT1299)~~
