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

梁啸, 叶盛波, 宋晨阳, 等. 基于分布式穿墙雷达的多目标自动检测方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24127
引用本文: 梁啸, 叶盛波, 宋晨阳, 等. 基于分布式穿墙雷达的多目标自动检测方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24127
LIANG Xiao, YE Shengbo, SONG Chenyang, et al. Automatic multitarget detection method based on distributed through-wall radar[J]. Journal of Radars, in press. doi: 10.12000/JR24127
Citation: LIANG Xiao, YE Shengbo, SONG Chenyang, et al. Automatic multitarget detection method based on distributed through-wall radar[J]. Journal of Radars, in press. 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
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

Automatic Multitarget Detection Method Based on Distributed Through-wall Radar

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

     

  • 图  1  雷达回波矩阵

    Figure  1.  The radar echo matrix

    图  2  处理步骤流程图

    Figure  2.  Flow chart of processing steps

    图  3  测距模块定位示意图

    Figure  3.  The positioning diagram of the UWB module

    图  4  仿真实验场景

    Figure  4.  The simulation experiment scenario

    图  5  时域预处理结果图

    Figure  5.  Plot of time domain preprocessing results

    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

    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)

    图  8  实验场景

    Figure  8.  Experimental scenario

    图  9  实验1时域预处理结果图

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

    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

    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)

    图  12  实验2中时域预处理结果图

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

    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

    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)

    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Cij列数据,并进行记录,返回(1)
    下载: 导出CSV

    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Xa 1.50 1.55 –0.05
    坐标中Ya 0 –0.05 0.05
    下载: 导出CSV

    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10.780.9825.6
    目标20.650.9140.0
    下载: 导出CSV

    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
    下载: 导出CSV

    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10.490.6940.8
    目标20.410.6148.8
    目标30.320.5675.0
    下载: 导出CSV

    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
    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
    下载: 导出CSV

    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10.590.7933.9
    目标20.500.7142.0
    下载: 导出CSV

    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
    下载: 导出CSV
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  • 收稿日期:  2024-06-20
  • 修回日期:  2024-09-03
  • 网络出版日期:  2024-10-11

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