基于WA-EMD算法的脉冲式超宽带雷达多目标生命体征检测  

Multiple-subject vital sign detection for impulse-radio ultra-wideband radar based on WA-EMD

作　　者：唐良勇[1] 赵恒[1] 张亚菊[1] Tang Liangyong Zhao Heng Zhang Yaju(School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094, China)

机构地区：[1]南京理工大学电子工程与光电技术学院,江苏南京210094

出　　处：《南京理工大学学报》2017年第2期198-206,共9页Journal of Nanjing University of Science and Technology

基　　金：国家科技支撑计划(2014BAK12B03)

摘　　要：针对脉冲式超宽带雷达的非接触式生命体征检测系统中多目标生命体征难以准确识别和分离的问题,该文提出一种基于窗平均经验模式分解(WA-EMD)的高分辨多目标生命体征分离和提取算法。WA-EMD算法利用加窗的方法来计算局部平均值,具有良好的抗模态混叠和抗噪声性能,能够准确地分离出不同人体目标的呼吸信号,实现同一距离门中多目标的有效检测。利用Hilbert变换获得呼吸信号的时变频率。仿真实验和雷达实测结果表明,该文提出的算法能够准确有效地实现同一距离门中多目标的生命体征的准确识别和分离,可计算实时的呼吸速率。In view of the problem that identification and separation of vital signs from multiplesubjects for the non-contact vital sign detection system of the impulse-radio ultra-wideband radar are difficult, a high-resolution algorithm based on the window average-empirical mode decomposition （WA-EMD） is proposed in this paper. The windowed method is used in the WA-EMD algorithm to calculate the local mean for eliminating the mode mixing and noise, and the respiratory signals from different subjects are separated accurately and the different subjects in a single range bin are identified effectively. The Hilbert transform is used to acquire the time-varying respiratory rate. Simulation and measurement results show that the vital signs of multiple subjects in the same range bin can be separated and identified accurately, and the real-time respiratory rate can be acquired.

关 键 词：超宽带雷达 窗平均经验模式分解法 时频分析 非接触式生命体征检测 

分 类 号：TN958.2[电子电信—信号与信息处理]