## 5 Results
### Experiment with Multiple Targets  
In the multi-target detection experiment, three targets stood in the detection environment, and their ranges from the radar were 3.61 m, 7.00 m, and 11.05 m, respectively. The schematic diagram of the positions is shown in Figure 10.  
The result processed by our proposed method is shown in Figure 11. After preprocessing, periodically varying strips related to the respiratory motion can be observed around 3.5 m, 7 m, and 11 m. The closer the target is, the stronger the energy scattered by the target. In addition, the frequency and intensity of the target's respiratory motion will also affect the intensity of the electromagnetic wave energy scattered by the target. After being processed by CNN, the features related to the respiratory movement are detected. Figure 10: Schematic diagram of the positions of the multi-target experiment. Figure 9: Comparison result processed by the method in [36] of the experiment with a single target. Three clusters of TPOIs are extracted in the range dimension. After simplifying the TPOIs using the clustering algorithm, the output target ranges are 3.62 m, 7.00 m, and 11.13 m, respectively. The average range error is 3 cm. Figure 12 shows the result processed by the method in [34]. The energy at the range of 3.83 m is the highest, which is regarded as the detection unit by the method. At the same time, there are energy spikes at the ranges of 1.07 m, 7.04 m, and 11.48 m. The correlation coefficients between them and the detection unit are all below the threshold of 0.6. They are all identified by the method as background clutter signals. Finally, the method only extracted Target 1, and the range error is 0.22 m. Target 2 and Target 3 were missed and have not been extracted. Figure 13 shows the result processed by the method in [35]. It can be seen that the method in [35] only extracts two targets. The output range of Target 1 is 3.64 m with a range error of 0.03 m, and the output range of Target 3 is 11.36 m with a distance range of 0.31 m. Target 2 was missed. Since the method in [35] is an energy-based extraction method, the accuracy of its extraction result largely depends on the energy intensity scattered by the target. From the range-frequency matrix, the signal of Target 2 can be observed at the range of about 7 m. However, the energy ratio here is much lower than the detection threshold \(T_{r}\) due to the weak energy compared to other targets. Therefore, Target 2 is not recognized as a target by the algorithm, and the algorithm gives an incorrect decision. Figure 11: Results of the experiment with multiple targets: (a) range-slow-time matrix obtained after step A; (b) TPOIs extracted by CNN. Figure 12: Comparison result processed by the method in [34] of the experiment with multiple targets, where the blue line represents 1D range profile used for detection and the green line represents the normalized correlation. Figure 14 shows the processed result by the method in [36]. The green and red lines represent the threshold signals with \(\rho\) of 20 and 10, respectively. It can be seen that because the threshold signal with \(\rho\) of 20 has a high detectable target amplitude, only Target 3 is extracted. The threshold signal with \(\rho\) of 10 has a low detectable target amplitude. All targets can be extracted, but there are false alarms at about 12 m. Since the energy scattered by the target is not only related to the range, the frequency and intensity of the target's respiratory motion will also affect the energy scattered by the target. Therefore, there are missed alarms and false alarms in multi-target detection through the range-dependent threshold. We randomly select 50 sample data from the validation set, and the results of the target extraction from the sample data by different methods are summarized in Table 3, in which the target is correctly extracted when the range error is within 30 cm.