面向通感一体化的变分模态分解-希尔伯特-黄变换呼吸频率感知算法  

Variational Mode Decomposition-Hilbert-Huang Transform Breathing Rate Sensing Algorithm for Integration of Sensing and Communication

作　　者：杨小龙 张亭亭 周牧 高铭 童睿轩 YANG Xiaolong;ZHANG Tingting;ZHOU Mu;GAO Ming;TONG Ruixuan(School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《电子与信息学报》2025年第4期1014-1025,共12页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62101085);重庆市教委科学技术研究项目(KJQN202400647);重庆市研究生科研创新项目(CYS240399)。

摘　　要：通感一体化(ISAC)作为一种6G关键技术,将通信和感知功能集成到Wi-Fi设备,为室内人体呼吸频率感知提供一种有效的方法。针对当前基于ISAC的呼吸频率感知存在鲁棒性低和“盲点”的问题,该文提出一种基于信号变分模态分解(VMD)-希尔伯特-黄变换(HHT)呼吸频率感知算法。首先,选择对环境感知敏感度较强的Wi-Fi链路构建信道状态信息(CSI)比值模型。其次,将滤波后的CSI比值时间序列的各子载波进行投影,结合幅相信息生成不同呼吸模式信号的候选集。再次,对于每一个子载波,根据周期性在候选集中选择一个短期呼吸噪声比最大的候选序列作为最终的呼吸模式,然后设置阈值选择子载波,并对其进行VMD和HHT时频分析,去除人体呼吸频率成分以外的模态分量,并重构剩余模态分量。在此基础上,利用主成分分析(PCA)对所有重构的子载波降维,选择方差贡献率达到99%以上的主成分分量,并使用ReliefF算法重新构建呼吸信号,得到融合信号。最后,对融合信号利用峰值检测算法计算呼吸频率。实验结果表明,该感知方法在会议办公室和走廊两种场景下的平均估计精度超过97%,显著提高了鲁棒性并克服了“盲点”问题,优于其他现有的感知方案。Objective Breathing rate is a vital physiological indicator of human health.Abnormal changes in this rate can signify diseases like chronic obstructive pulmonary disease,sleep apnea syndrome,and nocturnal hypoventilation syndrome.Timely and accurate detection of these changes can help identify health risks early,enable professional medical intervention,and optimize treatment timing,thereby improving overall health.However,current detection methods often face limitations due to noise interference and“blind spot”issues,which impact accuracy and robustness.To address these challenges,this paper employs Wi-Fi devices to measure indoor human breathing rates using Integrated Sensing And Communication(ISAC)technology.By combining Variational Modal Decomposition(VMD)and Hilbert-Huang Transform(HHT),a new breathing rate sensing algorithm is proposed.This approach aims to enhance detection accuracy and robustness,resolve the“blind spot”problem in existing technologies,and offer an efficient and reliable solution for health monitoring.Methods Wi-Fi links with high environmental sensitivity were selected to construct the Channel State Information(CSI)ratio model.Subcarriers of the filtered CSI ratio time series were projected,and amplitude and phase information were combined to generate a candidate set of breathing mode signals.For each subcarrier,the sequence with the highest short-term breath noise ratio,determined by periodicity,was identified as the final breath pattern.A threshold was then applied to select relevant subcarriers.Timefrequency analysis using VMD and HHT eliminated modal components unrelated to the human breath rate,and the remaining components were reconstructed.Principal Component Analysis(PCA)was applied for dimensionality reduction,selecting components accounting for over 99%of the variance.The ReliefF algorithm was subsequently used to reconstruct the breath signal into a fused signal,from which the breathing rate was calculated using a peak detection algorithm.Results and Discussions Experime

关 键 词：通感一体化 信道状态信息 呼吸频率 HILBERT-HUANG变换 变分模态分解 

分 类 号：TN929.5[电子电信—通信与信息系统]