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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。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.
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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
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 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)
表 1 基于生命信息的信号关联处理流程
Table 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 $ 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，否则进入步骤4 $ \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)； 4. 输出步骤3中获取的$ i,j $结果，即为匹配结果。 
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表 2 UWB测距模块测距定位结果(m)
Table 2. 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
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表 3 仿真实验中两通道中相同目标增强前后相关性
Table 3. In the simulation experiment, 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 0.78 0.98 25.6 目标2 0.65 0.91 40.0
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表 4 不同信噪比下的定位结果误差(m)
Table 4. 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 5 实验1中两通道中相同目标增强前后相关性
Table 5. 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 6 不同算法的运行时间、定位结果和平均定位精度对比
Table 6. Comparison of running time, localization results and average localization accuracy of different algorithms
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表 7 两通道中相同目标增强前后相关性
Table 7. 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 0.59 0.79 33.9 目标2 0.50 0.71 42.0
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表 8 双目标定位结果示意表(m)
Table 8. 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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