基于超宽带雷达及支持向量机的灾后人体呼吸信号识别方法与试验研究

樊哲宁; 朱嘉健; 王立新; 杜鹏; 张移; 谢海珠

doi:10.11899/zzfy20210320

基于超宽带雷达及支持向量机的灾后人体呼吸信号识别方法与试验研究

doi: 10.11899/zzfy20210320

樊哲宁^1,,
朱嘉健^{2, 3, ,},
王立新^{2, 3, 4},
杜鹏^{2, 3},
张移^{2, 3},
谢海珠¹

1.
香港城市大学深圳研究院, 广东深圳 518057
2.
广东省地震局中国地震局地震监测与减灾技术重点实验室, 广州 510070
3.
广东省地震局广东省地震预警与重大工程安全诊断重点实验室, 广州 510070
4.
深圳防灾减灾技术研究院, 广东深圳 518003

基金项目: 国家重点研发计划（2018YFC1504403）；广东省科技计划（2017B030314082）

详细信息

作者简介:
樊哲宁，女，生于1994年。硕士。主要从事数据挖掘、深度学习研究。E-mail：fanzheningchn@163.com

通讯作者:
朱嘉健，男，生于1989年。硕士。主要从事结构动力分析、结构健康监测研究。E-mail：zjjsysu@foxmail.com

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出版历程
- 收稿日期: 2021-05-11
- 刊出日期: 2021-09-30

An Approach and Experiments for Human Respiratory Signal Recognition based on UWB Radar and Support Vector Machine

Fan Zhening^1
,,
Zhu Jiajian^{2, 3
, ,},
Wang Lixin^{2, 3, 4},
Du Peng^{2, 3},
Zhang Yi^{2, 3},
Xie Haizhu¹

1.
Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, Guangdong, China
2.
CEA Key Laboratory of Earthquake Monitoring and Disaster Mitigation Technology, Guangdong Earthquake Agency, Guangzhou 510070, China
3.
Guangdong Provincial Key Laboratory of Earthquake Early Warning and Safety Diagnosis of Major Projects, Guangdong Earthquake Agency, Guangzhou 510070, China
4.
Shenzhen Academy of Disaster Prevention and Reduction, Shenzhen 518003, Guangdong, China

摘要

摘要: 与常规雷达相比，超宽带雷达具有距离分辨力高、近距离盲区小、穿透性强、目标识别率高等特点，已被广泛应用于灾后搜寻、救援工作中，以对受困生命体征目标进行生命探测。为实现使用超宽带雷达对受困生命体征目标的识别定位，本研究提出基于信号多特征提取技术及支持向量机模型的人体呼吸信号识别方法。首先，使用经验模态分解、变分模态分解及希尔伯特变换提取雷达探测信号的微多普勒特征，使用傅里叶变换提取宏观频谱特征，使用相关分析获取相关性特征；然后，以提取的信号特征为输入，使用支持向量机模型对信号进行分类，进而对人体呼吸信号进行识别，对人体位置进行定位。不同障碍物场景下的试验结果表明，本方法可有效识别砖墙、建筑楼板等遮挡物下的受困生命体征目标，并提供其位置信息。
- 超宽带雷达 /
- 生命探测 /
- 信号处理 /
- 支持向量机
Abstract: Compared with conventional radar, the ultra-wideband (UWB) radar has the advantages of high distance resolution, small blind spot at close range, strong penetration, and high target recognition rate. Therefore, it has been widely used in post-disaster Radar-based Life Detecting System. In order to identify and locate the trapped person using UWB radar, a method based on signal multi-feature extraction and support vector machine model for human respiratory signal recognition is proposed in this paper. Firstly, we use empirical mode decomposition, variational mode decomposition, and Hilbert transformation to extract the micro-Doppler characteristics of echo signals use Fourier transformation to extract the spectrum characteristics and use correlation analysis to obtain the correlation characteristics. Then, the signals are classified by a support vector machine model based on these signal features. As a result, the respiratory signal can be identified and the position of the human body can be located. The experimental results obtained from different scenes show that the proposed method can effectively identify the human body which is shielded by brick walls and floor slabs, and the location of the human body can be determined at the same time.
- Ultra-broadband radar /
- Life detection /
- Signal processing /
- Support vector machine

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图 1 支持向量与间隔

Figure 1. Support vector and margin

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图 2 人体呼吸信号识别算法流程

Figure 2. Flow of respiratory detection algorithm

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图 3 受困生命体征目标位置识别流程

Figure 3. Procedure of the trapped person localization

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图 4 PluseOn440单基站雷达传感器模块及平板天线

Figure 4. P440 monostatic radar module and panel antenna

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图 5 无障碍物场景的人体探测

Figure 5. Human body detection without obstacle

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图 6 无障碍物场景下探测距离为25 m时的识别结果

Figure 6. Detection results of the experiment without obstacles when the human body is 25 m away

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图 7 砖墙隔挡场景下的人体探测

Figure 7. Human body detection through brick wall

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图 8 砖墙隔挡场景下探测距离7.24 m时识别结果

Figure 8. Detection results of the experiment with the block of a brick wall when the human body is 7.24 m away

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图 9 楼板隔挡场景下的人体探测

Figure 9. Human body detection through floorslabs

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图 10 2层楼板隔挡场景下识别结果

Figure 10. Detection results of the experiment with the block of 2 floorslabs

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表 1 训练样本探测环境

Table 1. Scenarios for human detection

障碍物类别	障碍物厚度/cm	探测距离/m
无障碍	—	1、2、3、5、10、15、20
泡沫板	3	1、2、3、5、10、15、20
玻璃	1	1、2、3、5、10、15、20
木门	5	1、2、3、5、10、15、20
砖墙	28	1、2、3、5、10、15、20

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表 2 平板天线主要技术参数

Table 2. Technical parameters of panel antenna

参数名称	参数值
尺寸/mm	150×150×18
覆盖频段/GHz	3.25～3.75
3 dB角	30°
增益/dB	15

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表 3 无障碍物场景下分类准确度

Table 3. Accuracy of classification in experiments without obstacle

探测距离/m	准确度/%
探测距离/m	±10 cm误差	±30 cm误差
10	87	88
15	82	83
20	89	91
25	97	98
30	95	96

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表 4 砖墙隔挡场景下分类准确度

Table 4. Accuracy of classification in the experiments with the block of brick wall

探测距离/m	障碍物厚度/cm	准确度/%
探测距离/m	障碍物厚度/cm	±10 cm误差	±30 cm误差
0.24	24	72	72
1.24	24	81	81
3.24	24	72	73
5.24	24	85	86
7.24	24	83	81

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表 5 楼板隔挡场景下探测工况

Table 5. Scenarios for human detection with the block of floorslabs

障碍物层数	障碍物总厚度/m	探测距离/m	探测时长/min	被探测人员
1	0.1	0.50	3	男性1
2	0.2	1.53	3	男性1
1	0.1	0.50	5	男性1
1	0.1	0.50	5	男性2
1	0.1	0.50	5	男性3
2	0.2	1.53	5	男性1
2	0.2	1.53	5	男性2
2	0.2	1.53	5	男性3
2	0.2	1.53	5	男性1

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表 6 楼板隔挡场景下分类准确度

Table 6. Accuracy of classification in the experiments with the block of floorslabs

楼板层数	障碍物厚度/m	探测距离/m	准确度/%
楼板层数	障碍物厚度/m	探测距离/m	±10 cm误差	±30 cm误差
1	0.1	0.50	75	78
2	0.2	1.53	73	77

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