基于分布式穿墙雷达的多目标自动检测方法

梁啸; 叶盛波; 宋晨阳; 袁玉冰; 张群英; 刘小军; 姜和俊; 李红

doi:10.12000/JR24127

基于分布式穿墙雷达的多目标自动检测方法

DOI: 10.12000/JR24127

梁啸^{1, 2, 3},
叶盛波^{1, 2},
宋晨阳^{1, 2, 3},
袁玉冰^{1, 2, 3},
张群英^{1, 2},
刘小军^{1, 2, 3, ,},
姜和俊^4, ,,
李红⁴

1.
中国科学院空天信息创新研究院北京 100094
2.
中国科学院电磁辐射与探测技术重点实验室北京 100094
3.
中国科学院大学电子电气与通信工程学院北京 100049
4.
近地面探测技术重点实验室江苏无锡 214035

基金项目: 国家重点研发计划(2023YFC3011503)，近地面探测技术重点实验室基金(6142414220710)

详细信息

作者简介:
梁　啸，博士生，主要研究方向为超宽带穿墙雷达生命信号检测、生命信号增强技术等

叶盛波，博士，研究员，硕士生导师，主要研究方向为超宽带探地雷达、穿墙三维成像雷达等技术

宋晨阳，博士，助理研究员，主要研究方向为超宽带雷达、新材料天线、天线阵列去耦及射频识别技术等

袁玉冰，博士生，主要研究方向为超宽带雷达信号处理、穿墙三维成像技术等

张群英，博士，研究员，博士生导师，主要研究方向为微波探测新方法研究、超宽带雷达系统、雷达信号处理

刘小军，博士，研究员，博士生导师，主要研究方向为超宽带雷达技术、信号与信息处理

姜和俊，学士，高级工程师，主要研究方向为爆炸物探测技术和发展

李　红，硕士，工程师，主要研究方向为爆炸物探测技术和发展

通讯作者:
刘小军 lxjdr@mail.ie.ac.cn

姜和俊 jhj68@126.com

责任主编：崔国龙 Corresponding Editor: CUI Guolong
中图分类号: TN953
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- 被引次数: 0
出版历程
- 收稿日期: 2024-06-20
- 修回日期: 2024-09-03
- 网络出版日期: 2024-10-11

Automatic Multitarget Detection Method Based on Distributed Through-wall Radar

LIANG Xiao^{1, 2, 3},
YE Shengbo^{1, 2},
SONG Chenyang^{1, 2, 3},
YUAN Yubing^{1, 2, 3},
ZHANG Qunying^{1, 2},
LIU Xiaojun^{1, 2, 3
, ,},
JIANG Hejun^{4
, ,},
LI Hong⁴

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing 100094, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
4.
Science and Technology on Near-surface Detection Laboratory, Wuxi 214035, China

Funds: The National Key Research and Development Program of China (2023YFC3011503), Science and Technology on Near-surface Detection Laboratory (6142414220710)

More Information

Corresponding author: LIU Xiaojun, lxjdr@mail.ie.ac.cn; JIANG Hejun, jhj68@126.com

摘要

摘要: 超宽带雷达具有抗干扰能力强、穿透性强等特点，被广泛应用于穿墙人体目标探测。单发单收雷达具有体积小、重量轻的优势，但是无法实现目标的二维定位。MIMO阵列雷达能够实现对于目标的定位，但是存在着体积与分辨率之间的相互制约，同时运算时间较长。该文基于分布式穿墙雷达，提出了一种基于分布式雷达的多目标自动检测方法。首先，对回波信号进行时域预处理、时频转换等，基于恒虚警检测的目标距离测量方法获取目标候选距离单元，使用滤波矩阵进行候选信号增强；基于生命信息对增强后信号进行关联，实现目标匹配；最后使用定位模块来实现雷达位置自确定，进而实现生命目标位置的快速、自动检测。为了避免偶发误差对最终定位结果的影响，该文使用定位场景剖分的方法实现穿墙场景下的生命目标二维定位。实验结果表明，该文所提方法可以实现穿墙场景下多目标的检测定位，在实测数据中运算时间为0.95 s，优于其他对比方法4倍以上。
- 穿墙雷达 /
- 分布式雷达 /
- 无线组网 /
- 信号增强 /
- 目标检测 /
- 目标匹配 /
- 联合定位
Abstract: Ultra-WideBand (UWB) radar exhibits strong antijamming capabilities and high penetrability, making it widely used for through-wall human-target detection. Although single-transmitter, single-receiver radar offers the advantages of a compact size and lightweight design, it cannot achieve Two-Dimensional (2D) target localization. Multiple-Input Multiple-Output (MIMO) array radar can localize targets but faces a trade-off between size and resolution and involves longer computation durations. This paper proposes an automatic multitarget detection method based on distributed through-wall radar. First, the echo signal is preprocessed in the time domain and then transformed into the time-frequency domain. Target candidate distance cells are identified using a constant false alarm rate detection method, and candidate signals are enhanced using a filtering matrix. The enhanced signals are then correlated based on vital information, such as breathing, to achieve target matching. Finally, a positioning module is employed to determine the radar’s location, enabling rapid and automatic detection of the target’s location. To mitigate the effect of occasional errors on the final positioning results, a scene segmentation method is used to achieve 2D localization of human targets in through-wall scenarios. Experimental results demonstrate that the proposed method can successfully detect and localize multiple targets in through-wall scenarios, with a computation duration of 0.95 s based on the measured data. In particular, the method is over four times faster than other methods.
- Through-wall radar /
- Distributed radar /
- Wireless networking /
- Signal enhancement /
- Target detection /
- Target matching /
- Federated targeting

HTML全文

图 1 雷达回波矩阵

Figure 1. The radar echo matrix

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图 2 处理步骤流程图

Figure 2. Flow chart of processing steps

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图 3 测距模块定位示意图

Figure 3. The positioning diagram of the UWB module

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图 4 仿真实验场景

Figure 4. The simulation experiment scenario

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图 5 时域预处理结果图

Figure 5. Plot of time domain preprocessing results

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图 6 基于恒虚警检测的目标距离估计结果图

Figure 6. The target distance estimation results based on constant false alarm detection

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图 7 基于滤波矩阵的信号增强前后结果图(每幅子图中从上到下为距离排序)

Figure 7. Before and after result plots of signal enhancement based on filter matrix (each subplot is sorted by distance from top to bottom in each subplot)

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图 8 实验场景

Figure 8. Experimental scenario

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图 9 实验1时域预处理结果图

Figure 9. The time-domain preprocessing result plots in experiment1

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图 10 实验1中基于恒虚警检测的目标距离估计结果图

Figure 10. The target distance estimation results based on constant false alarm detection in experiment1

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图 11 实验1中基于滤波矩阵的信号增强前后结果图(每幅子图中从上到下为距离排序)

Figure 11. Before and after result plots of signal enhancement based on filter matrix (each subplot is sorted by distance from top to bottom in each subplot in experiment1)

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图 12 实验2中时域预处理结果图

Figure 12. The time-domain preprocessing result plots in experiment2

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图 13 实验2中基于恒虚警检测的目标距离估计结果图

Figure 13. The target distance estimation results based on constant false alarm detection in experiment2

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图 14 实验2中基于滤波矩阵的信号增强前后结果图(每幅子图中从上到下为距离排序)

Figure 14. Before and after result plots of signal enhancement based on filter matrix (each subplot is sorted by distance from top to bottom in each subplot in experiment2)

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1 基于生命信息的信号关联处理流程

1. The signal correlations based on vital information

输入：两个雷达预处理后的回波信号矩阵信号矩阵${{\boldsymbol{X}}_1}$, ${{\boldsymbol{X}}_2}$，检测结果中P个目标对应的距离结果$k_1^1,k_2^1, \cdots ,k_P^1 \in J$和$ k_1^2,k_2^2, \cdots ,k_P^2 \in J $
输出：步骤3中获取的$ i,j $结果，即为匹配结果。
1. 获取$ {\boldsymbol{R}}_{}^1(p,n) = {{\boldsymbol{X}}_1}\left( {k_p^1,k} \right) $, ${\boldsymbol{R}}_{}^2(p,n) = {{\boldsymbol{X}}_2}(k_p^2,k)$，将对应行标记为${\boldsymbol{r}}_p^1(n)$与$ {\boldsymbol{r}}_p^2(n) $
2. 求解相关性
$ {{\mathrm{cov}}} \left( {{\boldsymbol{r}}_i^1(m),{\boldsymbol{r}}_j^2(m)} \right) = \frac{{\left\| {\displaystyle\sum \limits_{m = 1}^M {\boldsymbol{r}}_i^1(m){{\left( {{\boldsymbol{r}}_j^2(m)} \right)} }} \right\|}}{{\sqrt {\displaystyle\sum \limits_{m = 1}^M {{\left( {\boldsymbol{{r}}_i^1(m)} \right)}^2}} \sqrt {\displaystyle \sum \limits_{m = 1}^M {{\left( {{\boldsymbol{r}}_j^2(m)} \right)}^2}} }},\quad 1 \le i,j \le P $
$ {{\mathrm{cov}}} \left( {{\boldsymbol{r}}_i^1(m),{\boldsymbol{r}}_j^2(m)} \right) $为$ {c_{ij}} $，得到相关矩阵C
3. 迭代进行目标的关联问题的求解，直至当前值不满足阈值$ \delta = 0.55 $
(1)获取$ {c_{ij}} $，当$ {c_{ij}} > \delta $继续，否则输出i, j
$ \begin{aligned} (i,j) =\;& \arg \mathop {\max }\limits_{i,j}\; {c_{ij}} \\ =\;& \mathop {\arg \max }\limits_{i,j}\; ({\boldsymbol{C}}),{\text{ }}i,j \in 1,2, \cdots ,P \\ \end{aligned} $
(2)去除C中i行j列数据，并进行记录，返回(1)

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表 1 UWB测距模块测距定位结果(m)

Table 1. UWB ranging module ranging and positioning results (m)

类别	真实距离	测量距离	误差值
标签1	1.56	1.55	0.01
标签2	1.56	1.56	0
坐标中X_a	1.50	1.55	–0.05
坐标中Y_a	0	–0.05	0.05

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表 2 仿真实验信号增强前后相同目标相关性对比

Table 2. Comparison of correlation between the same target before and after signal enhancement in simulation experiments

类别	增强前	增强后	增强百分比(%)
目标1	0.78	0.98	25.6
目标2	0.65	0.91	40.0

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表 3 不同信噪比下的定位结果误差(m)

Table 3. The error of localization of results under different SNRs (m)

类别	–10 dB	–8 dB	–6 dB	–4 dB	–2 dB	0 dB	2 dB	4 dB	6 dB	8 dB	10 dB	12 dB	14 dB
目标1	0.08	0.08	0.05	0.09	0.05	0.07	0.09	0.07	0.07	0.05	0.09	0.10	0.07
目标2	0.11	0.13	0.15	0.10	0.08	0.14	0.15	0.10	0.14	0.10	0.12	0.10	0.10

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表 4 实验1中两通道相同目标增强前后相关性

Table 4. In the experiment 1 the correlation before and after enhancement of the same target in both channels

类别	增强前	增强后	增强百分比(%)
目标1	0.49	0.69	40.8
目标2	0.41	0.61	48.8
目标3	0.32	0.56	75.0

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表 5 不同算法的运行时间、定位结果和平均定位精度对比

Table 5. Comparison of running time, localization results and average localization accuracy of different algorithms

方法	类别
方法	目标1 (m)	目标2 (m)	目标3 (m)	平均定位误差(m)	平均运算时间(s)
文献[14]方法	(–1.03, 5.91)	(1.85, 5.09)	(1.07, 7.66)	0.15	19.14
文献[11]方法	(–1.02, 5.92)	(1.91, 5.14)	(1.05, 7.57)	0.12	4.19
所提方法	(–1.02, 5.91)	(1.88, 5.12)	(1.07, 7.59)	0.12	0.95

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表 6 实验2两通道中相同目标增强前后相关性

Table 6. Correlation before and after enhancement of the same target in both channels in experiment 2

类别	增强前	增强后	增强百分比(%)
目标1	0.59	0.79	33.9
目标2	0.50	0.71	42.0

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表 7 双目标定位结果示意表(m)

Table 7. The table of results of dual-targeting (m)

类别	真实坐标	定位坐标	定位误差
目标1	(–1.00, 7.00)	(–1.08, 6.90)	0.13
目标2	(1.00, 5.00)	(0.96, 5.06)	0.07

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