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

杨小龙; 张亭亭; 周牧; 高铭; 童睿轩

doi:10.11999/JEIT240640

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

doi: 10.11999/JEIT240640

重庆邮电大学通信与信息工程学院重庆 400065

基金项目: 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)

详细信息

作者简介:
杨小龙：男，副教授，硕士生导师，研究方向为通感一体化，无线定位与感知

张亭亭：女，硕士生，研究方向为无线定位与感知

周牧：男，教授，博士生导师，研究方向为无线定位与感知，量子精密测量

高铭：女，博士生，研究方向为通感一体化

童睿轩：男，硕士生，研究方向为无线定位与感知

通讯作者:
周牧　zhoumu@cqupt.edu.cn

中图分类号: TN929.5
计量
- 文章访问数: 34
- HTML全文浏览量: 13
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2024-07-23
- 修回日期: 2024-12-12
- 网络出版日期: 2024-12-17

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

School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62101085), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202400647), Chongqing Graduate Student Research Innovation Project (CYS240399)

摘要

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。
- 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
Abstract: Integrated Sensing And Communication (ISAC) as a key technology for 6G integrates communication and sensing functions into Wi-Fi devices, providing an effective method for indoor human breath rate sensing. Addressing current challenges of low robustness and blind spots in ISAC-based breath rate sensing, a Variational Mode Decomposition (VMD)-Hilbert-Huang Transform (HHT) based algorithm for breath rate sensing is proposed in this paper. First, Wi-Fi links with high environmental sensitivity are selected to construct the Channel State Information (CSI) ratio model. Then, the subcarriers of the filtered CSI ratio time series are projected, and amplitude and phase information are combined to generate a candidate set of different breathing mode signals. Next, for each subcarrier, the candidate sequence with the highest short-term breath noise ratio is selected as the final breath pattern based on periodicity in the candidates. Then, a threshold is set to select subcarriers, followed by performing time-frequency analysis using VMD and HHT to remove modal components other than the human breath frequency, and reconstructing the remaining modal components. Subsequently, Principal Component Analysis (PCA) is employed to reduce the dimensionality of all reconstructed subcarriers, selecting principal components that contribute over 99% of the variance. The ReliefF algorithm is then used to reconstruct the breath signal as a fused signal. Finally, the breath rate is calculated using a peak detection algorithm on the fused signal. Experimental results show that the proposed detection method achieves the mean estimation accuracy of over 97% in both conference room and corridor scenarios, significantly enhancing robustness and overcoming the "blind spots" problem, outperforming other existing detection schemes.
- Integrated Sensing And Communications (ISAC) /
- Channel State Information (CSI) /
- Breathing rate /
- Hilbert-Huang Transform (HHT) /
- Variational Mode Decomposition (VMD)

HTML全文

图 1 Wi-Fi信号室内呼吸感知示意图

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图 2 呼吸频率感知算法系统框图

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图 3 Wi-Fi链路选择

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图 4 CSI投影图

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图 5 VMD-HHT时频分析

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图 6 实验场景和设备图

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图 7 步长对复杂度和精度的影响

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图 8 多天线收发系统实验结果对比

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图 9 会议办公室实测结果

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图 10 呼吸频率测量结果CDF图

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图 11 呼吸频率误差箱型图

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图 12 5 m时10次估计值

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图 13 走廊场景实测结果和算法复杂度比较

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表 1 收发机参数配置

参数	发射机	接收机
模式	Injection	Monitor
信道编号	64(5.32 GHz)
带宽	20 MHz
发包速率	100包/s
子载波个数	30
子载波编号	–58, –54,···, 54, 58
发射功率	15 dBm

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