## 5 Results  
### Experiment with a Single Target  
A human target stands 3.6 m away from the UWB impulse radar. The raw radar data are shown in Figure 5. Due to the strong scattered energy of the background, the raw data contain many static strips in the measuring time. The human respiration signal with a  
Figure 4: The scenarios for dataset collection: (**a**) Scenario 1: the standard brick wall; (**b**) Scenario 2: the multi-layer medium; (**c**) Scenario 3: the actual ruin.  
range of 3.6 m is overwhelmed by the background and noise signal, and the human target information cannot be directly extracted from the raw radar data. The result processed by our proposed method is shown in Figure 6. Figure 6a shows the result obtained after step A. After pre-processing, such as static background removal, high-frequency noise suppression, and signal enhancement, periodically varying strips corresponding to the respiratory-motion response can be observed at 3 to 4 m. After the feature extraction of the convolutional neural network, the output TPOIs are shown in Figure 6b. It should be noted that the output of the convolutional neural network is an 800-sized vector. For a more intuitive display, we duplicate it to generate a matrix of size 800 \(\times\) 600, keeping the same size as the range-slow-time matrix. The red dots represent the result of clustering, and the target range output by the method is 3.62 m.  
Furthermore, the detection method based on a 1D range profile in [34], the detection method based on a 2D range-frequency matrix in [35], and the method in [36] were performed for comparison with the proposed method. Figure 7 shows the result of the method in [34]. The blue line represents the 1D range profile for detection. The peak at the range of 3.63 m is regarded as the detection unit. The green line represents the correlation coefficients between the signal at different distances and the signal at 3.63 m. The scaling factor \(F\) and the threshold \(\rho_{th}\) in [34] are set to 2 and 0.6, respectively. The detection unit at 3.63 m is regarded as the human target signal. The peak at the range of 0.79 m is used to estimate the clutter energy, and its correlation coefficient is 0.17, which is lower than the threshold of 0.6.  
Figure 5: Raw radar data of the experiment with a single target.  
Figure 6: Results of the experiment with a single target: (**a**) range-slow-time matrix obtained after step A; (**b**) TPOIs extracted by CNN.  
Figure 8 shows the processed result by the method in [35]. Figure 8a is the range-frequency matrix used for detection, which is obtained by Fast Fourier transform (FFT) in the slow-time dimension. Figure 8b is the result extracted by the 2D CFAR energy windows, and the red dot represents the target. The threshold \(T_{r}\) in [35] is set to 1.5. It can be seen that the method in [35] also correctly extracts the human target, and the output target range is 3.59 m.  
Figure 9 shows the processed result by the method in [36]. A range-dependent threshold signal is generated, and hard thresholding is applied to the range profile. For the threshold signal, \(\rho\) is an important parameter that determines the minimum detectable target amplitude. The green and red lines represent the threshold signals with \(\rho\) of 20 and 10, respectively. As can be seen from Figure 9, the threshold signal with \(\rho\) of 10 can extract the target at 3.63 m. In the single-target detection experiment, all four methods correctly extract the target range.  
Figure 8: Comparison result processed by the method in [35] of the experiment with a single target: (**a**) range-frequency matrix used for detection; (**b**) result extracted by the 2D CFAR energy windows.  
Figure 7: Comparison result processed by the method in [34] of the experiment with a single target, where the blue line represents 1D range profile used for detection and the green line represents the normalized correlation.